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I was reviewing the train function in the quickstart-pytorch example and noticed a logical issue in how the average training loss is calculated.
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1
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28
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December 22, 2025
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Announcing Flower 1.25.0
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0
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35
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December 16, 2025
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Client selection in a single Flower federation with persistent client identities and metadata-based sampling
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0
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43
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December 15, 2025
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Running two or more trainings in parallel
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2
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46
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December 15, 2025
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the problem on docker when using flower
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1
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27
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December 2, 2025
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Announcing Flower 1.24.0
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1
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37
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December 1, 2025
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User authentication not anbled in superlink
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2
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32
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November 27, 2025
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Announcing Flower 1.23.0
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0
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47
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November 3, 2025
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Concurrent gRPC calls.
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1
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36
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October 30, 2025
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What is the difference between flwr.server and flwr.serverapp?
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2
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72
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October 23, 2025
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Hierarchical Federated Learning
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9
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444
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October 15, 2025
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flower-NLP-Raspberry PI
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8
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114
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October 9, 2025
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Announcing Flower 1.22.0
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0
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92
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September 22, 2025
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issue in dependency
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1
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54
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September 11, 2025
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Announcing Flower 1.21.0
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0
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83
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September 10, 2025
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Clients not initializing in authexample
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8
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76
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August 19, 2025
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Decentralized Federated Learning
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3
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180
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August 5, 2025
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Return a `List` of metrics from client to server
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0
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33
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August 5, 2025
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Announcing Flower 1.20.0
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0
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127
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July 29, 2025
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How to save local models?
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4
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64
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August 5, 2025
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How to Manage Learning Rate Scheduling (and similar operations) in Flower?
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5
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116
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July 24, 2025
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Strategy with multiple communication rounds per iteration
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7
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199
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July 16, 2025
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It seems that fit() is not called normally
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4
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59
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July 13, 2025
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Federated learning without communication
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1
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64
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July 13, 2025
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http error 429 thrown
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3
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129
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July 3, 2025
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Announcing Flower 1.19.0
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0
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91
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June 17, 2025
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Security Research Primitives and Attack Simulations in Flower
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0
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63
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June 12, 2025
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Flower Deployment Engine: "This request should be sent to a different service." when running local-deployment
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2
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65
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May 29, 2025
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Client fail to call fit function
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2
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87
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May 20, 2025
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Does anyone have experience applying federated learning to schools and student data?
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1
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61
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April 24, 2025
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