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Experiment architecture with the new flwr tool
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2
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132
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May 20, 2025
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How can I train more than one model on the server and send different parameters to different clients?
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0
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75
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April 22, 2025
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Not sure how to implement SecAgg(+) in this FL
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1
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96
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April 18, 2025
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Non-determinism only when number of clients is increased
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5
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231
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March 25, 2025
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Problem with BatchNormalization more precise with num_batches_tracked
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3
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165
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March 7, 2025
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Server still waiting while all clients crashes?
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3
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210
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March 5, 2025
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Address used in pyproject.toml
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3
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130
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February 26, 2025
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Actor dies unexpectedly in Flower simulation on HPC
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1
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266
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February 22, 2025
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How to dynamically update num_rounds on the server and ensure clients keep training
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3
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137
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February 21, 2025
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Model aggregation before all clients have finished the round
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0
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119
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January 22, 2025
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Increasing or in general suspicious high loss after first round of training
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8
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340
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January 27, 2025
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Scaffold implementation
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6
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448
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December 30, 2024
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Running the advanced pytorch example in google colab
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2
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190
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December 16, 2024
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Early Stopping Implementation
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7
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492
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November 22, 2024
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Clustered Federated Learning
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2
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342
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November 20, 2024
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Using MS COCO 2017 dataset for FL simulation
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3
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126
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November 4, 2024
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Sending truly arbritrary data from client to server
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5
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176
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October 27, 2024
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Stateful/Persistent clients in simulation context
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2
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230
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September 26, 2024
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How to prevent OOM error while training?
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3
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367
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August 5, 2024
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How to return aggregated confusion matrix and classification report
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3
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286
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May 31, 2024
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Server-Edges-Clients Settings
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3
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369
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April 30, 2024
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Freeze certain modules of the model
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2
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150
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April 29, 2024
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MLX send / get model parameters of complex model
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5
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575
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March 23, 2024
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How to handle client ids in Flower Next apps?
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3
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356
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March 22, 2024
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How to get the model parameters without aggregating?
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7
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469
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March 22, 2024
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How to control my updates to the server?
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4
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249
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March 22, 2024
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Issue with test-baseline.sh
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1
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157
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March 21, 2024
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Sending a large message to Edge Devices
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2
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228
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March 21, 2024
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Clarification on the "results" variable passed between aggregate_fit (server side) and fit (client side)
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0
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208
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February 21, 2024
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Random number of clients in every round of federated training
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0
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193
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February 21, 2024
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