diff --git a/.github/workflows/lint-test-build.yml b/.github/workflows/lint-test-build.yml
index c604358fa..d0f593b72 100644
--- a/.github/workflows/lint-test-build.yml
+++ b/.github/workflows/lint-test-build.yml
@@ -277,9 +277,9 @@ jobs:
           browser: chromium
           start: npm start
           install: false
-          wait-on: http://localhost:8080/
+          wait-on: http://localhost:8081/
           working-directory: ./web-client
-          config: baseUrl=http://localhost:8080/#/
+          config: baseUrl=http://localhost:8081/#/
 
   test-cli:
     needs:
diff --git a/discojs/discojs-core/src/client/event_connection.ts b/discojs/discojs-core/src/client/event_connection.ts
index 56d622453..d7b5f3179 100644
--- a/discojs/discojs-core/src/client/event_connection.ts
+++ b/discojs/discojs-core/src/client/event_connection.ts
@@ -126,7 +126,7 @@ export class WebSocketServer implements EventConnection {
 
     return await new Promise((resolve, reject) => {
       ws.onerror = (err: isomorphic.ErrorEvent) =>
-        reject(new Error(`connecting server: ${err.message}`)) // eslint-disable-line @typescript-eslint/restrict-template-expressions
+        reject(new Error(`Server unreachable: ${err.message}`)) // eslint-disable-line @typescript-eslint/restrict-template-expressions
       ws.onopen = () => resolve(server)
     })
   }
diff --git a/discojs/discojs-core/src/dataset/data/preprocessing/tabular_preprocessing.ts b/discojs/discojs-core/src/dataset/data/preprocessing/tabular_preprocessing.ts
index 34e4ddd67..c9509528e 100644
--- a/discojs/discojs-core/src/dataset/data/preprocessing/tabular_preprocessing.ts
+++ b/discojs/discojs-core/src/dataset/data/preprocessing/tabular_preprocessing.ts
@@ -18,10 +18,16 @@ interface TabularEntry extends tf.TensorContainerObject {
 const sanitize: PreprocessingFunction = {
   type: TabularPreprocessing.Sanitize,
   apply: (entry: tf.TensorContainer, task: Task): tf.TensorContainer => {
-    const { xs, ys } = entry as TabularEntry
-    return {
-      xs: xs.map(i => i === undefined ? 0 : i),
-      ys: ys
+    // if preprocessing a dataset without labels, then the entry is an array of numbers
+    if (Array.isArray(entry)) {
+      return entry.map(i => i === undefined ? 0 : i)
+    // otherwise it is an object with feature and labels
+    } else {
+      const { xs, ys } = entry as TabularEntry
+      return {
+        xs: xs.map(i => i === undefined ? 0 : i),
+        ys: ys
+      }
     }
   }
 }
diff --git a/discojs/discojs-core/src/dataset/dataset_builder.ts b/discojs/discojs-core/src/dataset/dataset_builder.ts
index 7acf15faa..572ebf920 100644
--- a/discojs/discojs-core/src/dataset/dataset_builder.ts
+++ b/discojs/discojs-core/src/dataset/dataset_builder.ts
@@ -94,7 +94,7 @@ export class DatasetBuilder<Source> {
   }
 
   async build (config?: DataConfig): Promise<DataSplit> {
-    // Require that at leat one source collection is non-empty, but not both
+    // Require that at least one source collection is non-empty, but not both
     if ((this._sources.length > 0) === (this.labelledSources.size > 0)) {
       throw new Error('Please provide dataset input files')
     }
diff --git a/discojs/discojs-core/src/informant/training_informant/base.ts b/discojs/discojs-core/src/informant/training_informant/base.ts
index edddb7a60..a62077c84 100644
--- a/discojs/discojs-core/src/informant/training_informant/base.ts
+++ b/discojs/discojs-core/src/informant/training_informant/base.ts
@@ -35,8 +35,12 @@ export abstract class Base {
     return this.messages.toArray()
   }
 
+  /**
+   *
+   * @returns the training round incremented by 1 (to start at 1 rather than 0)
+   */
   round (): number {
-    return this.currentRound
+    return this.currentRound + 1
   }
 
   participants (): number {
diff --git a/discojs/discojs-core/src/informant/training_informant/federated.ts b/discojs/discojs-core/src/informant/training_informant/federated.ts
index 4b7b7e8ee..67b0d73fc 100644
--- a/discojs/discojs-core/src/informant/training_informant/federated.ts
+++ b/discojs/discojs-core/src/informant/training_informant/federated.ts
@@ -12,7 +12,7 @@ export class FederatedInformant extends Base {
    * @param receivedStatistics statistics received from the server.
    */
   update (receivedStatistics: Record<string, number>): void {
-    this.currentRound = receivedStatistics.round
+    this.currentRound = receivedStatistics.round + 1
     this.currentNumberOfParticipants = receivedStatistics.currentNumberOfParticipants
     this.totalNumberOfParticipants = receivedStatistics.totalNumberOfParticipants
     this.averageNumberOfParticipants = receivedStatistics.averageNumberOfParticipants
diff --git a/discojs/discojs-core/src/training/disco.ts b/discojs/discojs-core/src/training/disco.ts
index 02788cb87..6263a8f06 100644
--- a/discojs/discojs-core/src/training/disco.ts
+++ b/discojs/discojs-core/src/training/disco.ts
@@ -110,11 +110,8 @@ export class Disco {
    * @param dataTuple The data tuple
    */
   async fit (dataTuple: data.DataSplit): Promise<void> {
-    this.logger.success('Thank you for your contribution. Data preprocessing has started')
-
     const trainData = dataTuple.train.preprocess().batch()
     const validationData = dataTuple.validation?.preprocess().batch() ?? trainData
-
     await this.client.connect()
     const trainer = await this.trainer
     await trainer.fitModel(trainData.dataset, validationData.dataset)
@@ -126,8 +123,6 @@ export class Disco {
   async pause (): Promise<void> {
     const trainer = await this.trainer
     await trainer.stopTraining()
-
-    this.logger.success('Training was successfully interrupted.')
   }
 
   /**
diff --git a/discojs/discojs-core/src/validation/validator.ts b/discojs/discojs-core/src/validation/validator.ts
index f308b284e..d47e91073 100644
--- a/discojs/discojs-core/src/validation/validator.ts
+++ b/discojs/discojs-core/src/validation/validator.ts
@@ -15,7 +15,7 @@ export class Validator {
     private readonly client?: clients.Client
   ) {
     if (source === undefined && client === undefined) {
-      throw new Error('cannot identify model')
+      throw new Error('To initialize a Validator, either or both a source and client need to be specified')
     }
   }
 
@@ -30,46 +30,42 @@ export class Validator {
   async assess (data: data.Data, useConfusionMatrix?: boolean): Promise<Array<{groundTruth: number, pred: number, features: Features}>> {
     const batchSize = this.task.trainingInformation?.batchSize
     if (batchSize === undefined) {
-      throw new TypeError('batch size is undefined')
+      throw new TypeError('Batch size is undefined')
     }
 
     const model = await this.getModel()
 
     let features: Features[] = []
     const groundTruth: number[] = []
-    const predictions: number[] = []
 
     let hits = 0
-    await data.preprocess().batch().dataset.forEachAsync((e) => {
-      if (typeof e === 'object' && 'xs' in e && 'ys' in e) {
-        const xs = e.xs as tf.Tensor
-
-        const ys = this.getLabel(e.ys as tf.Tensor)
-        const pred = this.getLabel(model.predict(xs, { batchSize }) as tf.Tensor)
-
-        const currentFeatures = xs.arraySync()
-
-        if (Array.isArray(currentFeatures)) {
-          features = features.concat(currentFeatures)
+    // Get model predictions per batch and flatten the result
+    // Also build the features and groudTruth arrays
+    const predictions: number[] = (await data.preprocess().dataset.batch(batchSize)
+      .mapAsync(async e => {
+        if (typeof e === 'object' && 'xs' in e && 'ys' in e) {
+          const xs = e.xs as tf.Tensor
+          const ys = this.getLabel(e.ys as tf.Tensor)
+          const pred = this.getLabel(model.predict(xs, { batchSize }) as tf.Tensor)
+
+          const currentFeatures = await xs.array()
+          if (Array.isArray(currentFeatures)) {
+            features = features.concat(currentFeatures)
+          } else {
+            throw new TypeError('Data format is incorrect')
+          }
+          groundTruth.push(...Array.from(ys))
+          this.size += xs.shape[0]
+          hits += List(pred).zip(List(ys)).filter(([p, y]) => p === y).size
+          // TODO: Confusion Matrix stats
+          const currentAccuracy = hits / this.size
+          this.graphInformant.updateAccuracy(currentAccuracy)
+          return Array.from(pred)
         } else {
-          throw new TypeError('features array is not correct')
+          throw new Error('Input data is missing a feature or the label')
         }
+      }).toArray()).flat()
 
-        groundTruth.push(...Array.from(ys))
-        predictions.push(...Array.from(pred))
-
-        this.size += xs.shape[0]
-
-        hits += List(pred).zip(List(ys)).filter(([p, y]) => p === y).size
-
-        // TODO: Confusion Matrix stats
-
-        const currentAccuracy = hits / this.size
-        this.graphInformant.updateAccuracy(currentAccuracy)
-      } else {
-        throw new Error('missing feature/label in dataset')
-      }
-    })
     this.logger.success(`Obtained validation accuracy of ${this.accuracy}`)
     this.logger.success(`Visited ${this.visitedSamples} samples`)
 
@@ -92,21 +88,35 @@ export class Validator {
       .toArray()
   }
 
-  async predict (data: data.Data): Promise<number[]> {
+  async predict (data: data.Data): Promise<Array<{features: Features, pred: number}>> {
     const batchSize = this.task.trainingInformation?.batchSize
     if (batchSize === undefined) {
-      throw new TypeError('batch size is undefined')
+      throw new TypeError('Batch size is undefined')
     }
 
     const model = await this.getModel()
-    const predictions: number[] = []
+    let features: Features[] = []
+
+    // Get model prediction per batch and flatten the result
+    // Also incrementally build the features array
+    const predictions: number[] = (await data.preprocess().dataset.batch(batchSize)
+      .mapAsync(async e => {
+        const xs = e as tf.Tensor
+        const currentFeatures = await xs.array()
+
+        if (Array.isArray(currentFeatures)) {
+          features = features.concat(currentFeatures)
+        } else {
+          throw new TypeError('Data format is incorrect')
+        }
 
-    await data.dataset
-      .batch(batchSize)
-      .forEachAsync(e =>
-        predictions.push(...(model.predict(e as tf.Tensor, { batchSize: batchSize }) as tf.Tensor).argMax(1).arraySync() as number[]))
+        const pred = this.getLabel(model.predict(xs, { batchSize }) as tf.Tensor)
+        return Array.from(pred)
+      }).toArray()).flat()
 
-    return predictions
+    return List(features).zip(List(predictions))
+      .map(([f, p]) => ({ features: f, pred: p }))
+      .toArray()
   }
 
   async getModel (): Promise<tf.LayersModel> {
@@ -118,7 +128,7 @@ export class Validator {
       return await this.client.getLatestModel()
     }
 
-    throw new Error('cannot identify model')
+    throw new Error('Could not load the model')
   }
 
   get accuracyData (): List<number> {
diff --git a/web-client/package.json b/web-client/package.json
index 1f75fa17e..873643eea 100644
--- a/web-client/package.json
+++ b/web-client/package.json
@@ -2,7 +2,7 @@
   "name": "@epfml/disco-web-client",
   "private": true,
   "scripts": {
-    "start": "vue-cli-service serve",
+    "start": "vue-cli-service serve  --port 8081",
     "build": "vue-cli-service build",
     "lint": "vue-cli-service lint",
     "test": "vue-cli-service test:unit tests"
diff --git a/web-client/src/components/pages/NotFound.vue b/web-client/src/components/pages/NotFound.vue
index ff7396ace..4d4f1b347 100644
--- a/web-client/src/components/pages/NotFound.vue
+++ b/web-client/src/components/pages/NotFound.vue
@@ -19,10 +19,10 @@
         >
           <TitleCard>
             <template #title>
-              Page Not Found
+              404 - Page Not Found
             </template>
             <template #text>
-              The page you asked for does not exist anymore.
+              The page you're looking for does not exist.
               <div class="grid grid-cols-2 gap-8 items-center">
                 <div class="text-right">
                   <CustomButton
diff --git a/web-client/src/components/sidebar/ModelLibrary.vue b/web-client/src/components/sidebar/ModelLibrary.vue
index 1c7098f2d..24394420b 100644
--- a/web-client/src/components/sidebar/ModelLibrary.vue
+++ b/web-client/src/components/sidebar/ModelLibrary.vue
@@ -144,7 +144,7 @@ export default defineComponent({
 
     openTesting (path: Path) {
       this.validationStore.setModel(path)
-      this.$router.push({ path: '/testing' })
+      this.$router.push({ path: '/evaluate' })
     },
 
     async downloadModel (path: Path) {
diff --git a/web-client/src/components/task_creation_form/containers/FileContainer.vue b/web-client/src/components/task_creation_form/containers/FileContainer.vue
index 00cd182d4..f62ccb05a 100644
--- a/web-client/src/components/task_creation_form/containers/FileContainer.vue
+++ b/web-client/src/components/task_creation_form/containers/FileContainer.vue
@@ -21,7 +21,7 @@
             >
               <path
                 d="M16.88 9.1A4 4 0 0 1 16 17H5a5 5 0 0 1-1-9.9V7a3 3 0 0 1 4.52-2.59A4.98 4.98 0 0 1 17 8c0 .38-.04.74-.12 1.1zM11 11h3l-4-4-4 4h3v3h2v-3z"
-              /></svg><span class="block text-gray-400 font-normal">Drag and drop your file anywhere or</span>
+              /></svg><span class="block text-gray-400 font-normal">Drag and drop your file anywhere</span>
             <span class="block text-gray-400 font-normal">or</span>
             <label
               :for="`hidden_${field.id}`"
diff --git a/web-client/src/components/testing/Tester.vue b/web-client/src/components/testing/Tester.vue
index 2163d1d34..b11391e56 100644
--- a/web-client/src/components/testing/Tester.vue
+++ b/web-client/src/components/testing/Tester.vue
@@ -27,7 +27,7 @@
         @action="predictUsingModel()"
       >
         <template #title>
-          Predic using model
+          Predict using model
         </template>
         <template #text>
           By clicking the button below, you will be able to predict using the selected model with chosen dataset of yours.
@@ -106,7 +106,7 @@
         </div>
         <div
           v-else
-          class="mx-auto lg:w-1/2 h-full bg-white rounded-md max-h-128 overflow-x-scroll overflow-y-hidden"
+          class="mx-auto lg:w-3/4 h-full bg-white rounded-md max-h-128 overflow-x-scroll overflow-y-hidden"
         >
           <TableLayout
             :columns="featuresNames"
@@ -246,6 +246,7 @@ import { useToaster } from '@/composables/toaster'
 import ButtonCard from '@/components/containers/ButtonCard.vue'
 import CustomButton from '@/components/simple/CustomButton.vue'
 import ImageCard from '@/components/containers/ImageCard.vue'
+import IconCard from '@/components/containers/IconCard.vue'
 import TableLayout from '@/components/containers/TableLayout.vue'
 import { List } from 'immutable'
 import * as d3 from 'd3'
@@ -307,22 +308,37 @@ async function getValidator (): Promise<Validator | undefined> {
   return new Validator(props.task, new ConsoleLogger(), memory.value, validationStore.model)
 }
 
+function handleDatasetBuildError (e: Error) {
+  console.log(e.message)
+  if (e.message.includes('provided in columnConfigs does not match any of the column names')) {
+    // missing field is specified between two "quotes"
+    const missingFields: String = e.message.split('"')[1].split('"')[0]
+    toaster.error(`The input data is missing the field "${missingFields}"`)
+  } else {
+    toaster.error('Incorrect data format. Please check the expected format at the previous step.')
+  }
+}
+
 async function predictUsingModel (): Promise<void> {
-  if (props.datasetBuilder?.size() === 0) {
-    return toaster.error('No file was given')
+  if (props.datasetBuilder?.size === 0) {
+    return toaster.error('Upload a dataset first')
   }
 
   const v = await getValidator()
   if (v !== undefined) {
     validator.value = v
   } else {
-    return toaster.error('Model was not found')
+    return toaster.error('No model found')
+  }
+  let testingSet: data.Data
+  try {
+    testingSet = (await props.datasetBuilder.build({ inference: true })).train
+  } catch (e) {
+    handleDatasetBuildError(e)
+    return
   }
 
-  const testingSet: data.Data = (await props.datasetBuilder.build({ inference: true })).train
-
-  toaster.success('Model prediction started')
-
+  toaster.info('Model prediction started')
   const predictions = await validator.value?.predict(testingSet)
 
   if (isImageTaskType.value) {
@@ -332,24 +348,30 @@ async function predictUsingModel (): Promise<void> {
     featuresNames.value = [...props.task.trainingInformation.inputColumns, 'Predicted_' + props.task.trainingInformation.outputColumns]
     dataWithPred.value = predictions.map(pred => ({ data: [...(pred.features as number[]), pred.pred] }))
   }
+  toaster.success('Model prediction finished successfully!')
 }
 
 async function assessModel (): Promise<void> {
   if (props.datasetBuilder?.size === 0) {
-    return toaster.error('No file was given')
+    return toaster.error('Upload a dataset first')
   }
 
   const v = await getValidator()
   if (v !== undefined) {
     validator.value = v
   } else {
-    return toaster.error('Model was not found')
+    return toaster.error('No model found')
   }
 
-  const testingSet: data.Data = (await props.datasetBuilder.build()).train
-
-  toaster.success('Model testing started')
+  let testingSet: data.Data
+  try {
+    testingSet = (await props.datasetBuilder.build()).train
+  } catch (e) {
+    handleDatasetBuildError(e)
+    return
+  }
 
+  toaster.info('Model testing started')
   try {
     const assessmentResults = await validator.value?.assess(testingSet)
 
@@ -360,6 +382,7 @@ async function assessModel (): Promise<void> {
       featuresNames.value = [...props.task.trainingInformation.inputColumns, 'Predicted_' + props.task.trainingInformation.outputColumns, 'Target_' + props.task.trainingInformation.outputColumns]
       dataWithPred.value = assessmentResults.map(pred => ({ data: [...(pred.features as number[]), pred.pred, pred.groundTruth] }))
     }
+    toaster.success('Model testing finished successfully!')
   } catch (e) {
     toaster.error(e instanceof Error ? e.message : e.toString())
   }
@@ -382,7 +405,7 @@ function openMapModal (prediction: number, groundTruth?: number) {
 function saveCsv () {
   let csvData: string
 
-  if (isImageTaskType) {
+  if (isImageTaskType.value) {
     if (props.groundTruth) {
       const rows = dataWithPred.value.map(el => [(el.data as ImageWithUrl).name, String(el.prediction), String(el.groundTruth)])
       csvData = d3.csvFormatRows([['Filename', 'Prediction', 'Ground Truth'], ...rows])
@@ -391,7 +414,9 @@ function saveCsv () {
       csvData = d3.csvFormatRows([['Filename', 'Prediction'], ...rows])
     }
   } else {
-    csvData = d3.csvFormatRows([(Object.values(featuresNames.value) as string[]), ...dataWithPred.value.map(el => Object.values(el.data).map(String))])
+    const featureNames = Object.values(featuresNames.value) as string[]
+    const predictionsWithLabels = dataWithPred.value.map(el => Object.values(el.data).map(String))
+    csvData = d3.csvFormatRows([featureNames, ...predictionsWithLabels])
   }
 
   const blob = new Blob([csvData], { type: 'text/csv;charset=utf-8;' })
diff --git a/web-client/src/components/training/Description.vue b/web-client/src/components/training/Description.vue
index 83f7e580a..0ef65d95e 100644
--- a/web-client/src/components/training/Description.vue
+++ b/web-client/src/components/training/Description.vue
@@ -96,6 +96,8 @@ import { trainingInformation, privacyParameters, modelCompileData, FormField, Fo
 import ModelCaching from './ModelCaching.vue'
 import IconCard from '@/components/containers/IconCard.vue'
 import DropdownCard from '@/components/containers/DropdownCard.vue'
+import Tasks from '@/assets/svg/Tasks.vue'
+import Model from '@/assets/svg/Model.vue'
 
 interface Props {
   task: Task
diff --git a/web-client/src/components/training/Finished.vue b/web-client/src/components/training/Finished.vue
index 8ab85c14f..6cf99ffc5 100644
--- a/web-client/src/components/training/Finished.vue
+++ b/web-client/src/components/training/Finished.vue
@@ -52,7 +52,7 @@ interface Props {
 const memoryStore = useMemoryStore()
 const validationStore = useValidationStore()
 const router = useRouter()
-const toast = useToaster()
+const toast = useToaster() // TODO: use this.$toaster
 
 const props = defineProps<Props>()
 
@@ -68,7 +68,7 @@ const modelInfo = computed<ModelInfo>(() => {
 async function testModel () {
   if (await memory.value.contains(modelInfo.value)) {
     validationStore.setModel(this.memory.pathFor(this.modelInfo))
-    router.push({ path: '/testing' })
+    router.push({ path: '/evaluate' })
   } else {
     toast.error('Model was not trained!')
   }
diff --git a/web-client/src/components/training/Trainer.vue b/web-client/src/components/training/Trainer.vue
index 755787ddb..b9040ac21 100644
--- a/web-client/src/components/training/Trainer.vue
+++ b/web-client/src/components/training/Trainer.vue
@@ -145,13 +145,21 @@ export default defineComponent({
         try {
           this.dataset = await this.datasetBuilder.build()
         } catch (e) {
-          this.$toast.error(e instanceof Error ? e.message : e.toString())
-          console.error(e)
+          console.error(e.message)
+          if (e.message.includes('provided in columnConfigs does not match any of the column names')) {
+            // missing field is specified between two "quotes"
+            const missingFields: String = e.message.split('"')[1].split('"')[0]
+            this.$toast.error(`The input data is missing the field "${missingFields}"`)
+          } else {
+            this.$toast.error('Incorrect data format. Please check the expected format at the previous step.')
+          }
           this.cleanState()
           return
         }
       }
 
+      this.$toast.info('Model training started')
+
       try {
         this.startedTraining = true
         await this.disco.fit(this.dataset)
@@ -161,6 +169,7 @@ export default defineComponent({
         console.error(e)
         this.cleanState()
       }
+      this.$toast.success('Training successfully completed')
     },
     cleanState (): void {
       this.distributedTraining = false
diff --git a/web-client/src/components/training/TrainingInformation.vue b/web-client/src/components/training/TrainingInformation.vue
index 0d56f423c..50d406e96 100644
--- a/web-client/src/components/training/TrainingInformation.vue
+++ b/web-client/src/components/training/TrainingInformation.vue
@@ -1,26 +1,64 @@
 <template>
   <div class="space-y-4 md:space-y-8">
+    <!-- Fancy training statistics -->
+    <div class="flex flex-wrap justify-center gap-4 md:gap-8">
+      <IconCardSmall class="w-72 shrink-0">
+        <template #header>
+          {{ currentRoundText }}
+        </template>
+        <template #text>
+          <!-- rounds normally start at 0 but here have already been incremented
+          to start at 1 in the UI -->
+          {{ trainingInformant.round() }}
+        </template>
+        <template #icon>
+          <Timer />
+        </template>
+      </IconCardSmall>
+      <IconCardSmall class="w-72 shrink-0">
+        <template #header>
+          Current # of participants
+        </template>
+        <template #text>
+          {{ trainingInformant.participants() }}
+        </template>
+        <template #icon>
+          <People />
+        </template>
+      </IconCardSmall>
+      <IconCardSmall class="w-72 shrink-0">
+        <template #header>
+          Average # of participants
+        </template>
+        <template #text>
+          {{ trainingInformant.averageParticipants() }}
+        </template>
+        <template #icon>
+          <People />
+        </template>
+      </IconCardSmall>
+    </div>
+
+    <!-- Training and validation accuracy charts -->
     <div
       class="flex flex-col md:grid gap-4 md:gap-8"
       :class="hasValidationData ? 'md:grid-cols-2' : ''"
     >
-      <!-- Validation Accuracy users chart -->
-      <IconCard
-        v-if="hasValidationData"
-      >
+      <!-- Training Accuracy users chart -->
+      <IconCard>
         <!-- Card header -->
         <template #title>
           {{
-            $t('training.trainingInformationFrame.accuracyCharts.validationAccuracyHeader')
+            $t('training.trainingInformationFrame.accuracyCharts.trainingAccuracyHeader')
           }}
         </template>
         <template #content>
           <span class="text-2xl font-medium text-slate-500">
-            {{ currentValidationAccuracy }}
+            {{ currentTrainingAccuracy }}
           </span>
           <span class="text-sm font-medium text-slate-500">
             {{
-              $t('training.trainingInformationFrame.accuracyCharts.validationAccuracyText')
+              $t('training.trainingInformationFrame.accuracyCharts.trainingAccuracyText')
             }}
           </span>
           <!-- Chart -->
@@ -29,26 +67,28 @@
             height="200"
             type="area"
             :options="options"
-            :series="validationAccuracyData"
+            :series="trainingAccuracyData"
           />
         </template>
       </IconCard>
 
-      <!-- Training Accuracy users chart -->
-      <IconCard>
+      <!-- Validation Accuracy users chart -->
+      <IconCard
+        v-if="hasValidationData"
+      >
         <!-- Card header -->
         <template #title>
           {{
-            $t('training.trainingInformationFrame.accuracyCharts.trainingAccuracyHeader')
+            $t('training.trainingInformationFrame.accuracyCharts.validationAccuracyHeader')
           }}
         </template>
         <template #content>
           <span class="text-2xl font-medium text-slate-500">
-            {{ currentTrainingAccuracy }}
+            {{ currentValidationAccuracy }}
           </span>
           <span class="text-sm font-medium text-slate-500">
             {{
-              $t('training.trainingInformationFrame.accuracyCharts.trainingAccuracyText')
+              $t('training.trainingInformationFrame.accuracyCharts.validationAccuracyText')
             }}
           </span>
           <!-- Chart -->
@@ -57,7 +97,7 @@
             height="200"
             type="area"
             :options="options"
-            :series="trainingAccuracyData"
+            :series="validationAccuracyData"
           />
         </template>
       </IconCard>
@@ -88,43 +128,6 @@
         </div>
       </template>
     </IconCard>
-
-    <!-- Fancy training statistics -->
-    <div class="flex flex-wrap justify-center gap-4 md:gap-8">
-      <IconCardSmall class="w-72 shrink-0">
-        <template #header>
-          {{ currentRoundText }}
-        </template>
-        <template #text>
-          {{ trainingInformant.round() }}
-        </template>
-        <template #icon>
-          <Timer />
-        </template>
-      </IconCardSmall>
-      <IconCardSmall class="w-72 shrink-0">
-        <template #header>
-          Current # of participants
-        </template>
-        <template #text>
-          {{ trainingInformant.participants() }}
-        </template>
-        <template #icon>
-          <People />
-        </template>
-      </IconCardSmall>
-      <IconCardSmall class="w-72 shrink-0">
-        <template #header>
-          Average # of participants
-        </template>
-        <template #text>
-          {{ trainingInformant.averageParticipants() }}
-        </template>
-        <template #icon>
-          <People />
-        </template>
-      </IconCardSmall>
-    </div>
   </div>
 </template>
 
@@ -145,7 +148,6 @@ interface Props {
   hasValidationData: boolean
 }
 const props = defineProps<Props>()
-
 const options = computed(() => chartOptions)
 const currentRoundText = computed(() =>
   props.trainingInformant.isDecentralized() || props.trainingInformant.isFederated()
diff --git a/web-client/src/main.ts b/web-client/src/main.ts
index 2058fe22b..3d936696e 100644
--- a/web-client/src/main.ts
+++ b/web-client/src/main.ts
@@ -1,7 +1,7 @@
 import { createApp } from 'vue'
 import { createPinia } from 'pinia'
 import VueApexCharts from 'vue3-apexcharts'
-import Toaster from '@meforma/vue-toaster'
+import { createToaster, Toaster } from '@meforma/vue-toaster'
 
 import App from '@/components/App.vue'
 import { router } from '@/router'
@@ -18,6 +18,20 @@ tf.ready()
 
 // create vue app
 const app = createApp(App)
+
+// Global error handler
+app.config.errorHandler = (err, instance, info) => {
+  const toaster = createToaster({ duration: 5000 })
+  if (err instanceof TypeError) {
+    // Implementation bug
+    toaster.error('Sorry, something went wrong on our side. Please reach out on slack.')
+  } else {
+    // Unknown error
+    toaster.error('Something went wrong. Please try again later or reach out on slack.')
+  }
+  console.error(err, instance, info)
+}
+
 const pinia = createPinia()
 const i18n = createCustomI18n()
 app
diff --git a/web-client/src/router/router.ts b/web-client/src/router/router.ts
index dea3b9d31..c2945fe80 100644
--- a/web-client/src/router/router.ts
+++ b/web-client/src/router/router.ts
@@ -15,7 +15,7 @@ import Tutorial from '@/components/information/Tutorial.vue'
 import Further from '@/components/information/Further.vue'
 import AboutUs from '@/components/pages/AboutUs.vue'
 
-export const router = createRouter({
+const router = createRouter({
   history: createWebHashHistory(process.env.BASE_URL),
   routes: [
     {
@@ -96,6 +96,21 @@ export const router = createRouter({
       path: '/:pathMatch(.*)*',
       name: 'not-found',
       component: NotFound
+    },
+    {
+      path: '/not-found',
+      name: 'not-found',
+      component: NotFound
     }
   ]
 })
+
+// Handle router errors
+router.onError((err) => {
+  // Handle the router error here
+  console.error('Router error:', err)
+  // Add code for reporting or other error handling logic
+  router.push({ path: '/not-found' })
+})
+
+export { router }
diff --git a/web-client/src/store/tasks.ts b/web-client/src/store/tasks.ts
index 4f9b2b8b1..aa71da032 100644
--- a/web-client/src/store/tasks.ts
+++ b/web-client/src/store/tasks.ts
@@ -1,7 +1,7 @@
 import { defineStore } from 'pinia'
 import { shallowRef } from 'vue'
 import { Map } from 'immutable'
-
+import { useToaster } from '@/composables/toaster'
 import { TaskID, Task, fetchTasks } from '@epfml/discojs'
 
 import { CONFIG } from '@/config'
@@ -22,10 +22,10 @@ export const useTasksStore = defineStore('tasks', () => {
       const tasks = await fetchTasks(CONFIG.serverUrl)
       tasks.forEach(addTask)
     } catch (e) {
-      console.error(
-        'Fetching of tasks failed with error',
-        e instanceof Error ? e.message : e.toString()
-      )
+      console.error('Fetching of tasks failed with error',
+        e instanceof Error ? e.message : e.toString())
+      const toaster = useToaster()
+      toaster.error('The server is unreachable. \n Please try again later or reach out on slack.')
     }
   }