Papers › Improving Generalization in Federated Learning by Seeking Flat Minima
Improving Generalization in Federated Learning by Seeking Flat Minima
Debora Caldarola, Barbara Caputo, Marco Ciccone
Models trained in federated settings often suffer from degraded performances and fail at generalizing, especially when facing heterogeneous scenarios. In this work, we investigate such behavior through the lens of geometry of the loss and Hessian eigenspectrum, linking the model's lack of generalization capacity to the sharpness of the solution. Motivated by prior studies connecting the sharpness of the loss surface and the generalization gap, we show that i) training clients locally with Sharpness-Aware Minimization (SAM) or its adaptive version (ASAM) and ii) averaging stochastic weights (SWA) on the server-side can substantially improve generalization in Federated Learning and help bridging the gap with centralized models. By seeking parameters in neighborhoods having uniform low loss, the model converges towards flatter minima and its generalization significantly improves in both homogeneous and heterogeneous scenarios. Empirical results demonstrate the effectiveness of those optimizers across a variety of benchmark vision datasets (e.g. CIFAR10/100, Landmarks-User-160k, IDDA) and tasks (large scale classification, semantic segmentation, domain generalization).
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Federated Learning | CIFAR-100 (alpha=0, 10 clients per round) | FedASAM + SWA | ACC@1-100Clients | 42.64 | #1 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0, 10 clients per round) | FedASAM | ACC@1-100Clients | 39.76 | #2 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0, 10 clients per round) | FedSAM + SWA | ACC@1-100Clients | 39.51 | #3 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0, 10 clients per round) | FedSAM | ACC@1-100Clients | 36.93 | #4 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0, 10 clients per round) | FedAvg | ACC@1-100Clients | 36.74 | #5 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0, 20 clients per round) | FedASAM + SWA | ACC@1-100Clients | 41.62 | #1 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0, 20 clients per round) | FedASAM | ACC@1-100Clients | 40.81 | #2 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0, 20 clients per round) | FedSAM + SWA | ACC@1-100Clients | 39.24 | #3 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0, 20 clients per round) | FedAvg | ACC@1-100Clients | 38.59 | #4 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0, 20 clients per round) | FedSAM | ACC@1-100Clients | 38.56 | #5 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0, 5 clients per round) | FedASAM + SWA | ACC@1-100Clients | 42.01 | #1 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0, 5 clients per round) | FedSAM + SWA | ACC@1-100Clients | 39.3 | #2 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0, 5 clients per round) | FedASAM | ACC@1-100Clients | 36.04 | #3 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0, 5 clients per round) | FedSAM | ACC@1-100Clients | 31.04 | #4 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0, 5 clients per round) | FedAvg | ACC@1-100Clients | 30.25 | #5 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 10 clients per round) | FedASAM + SWA | ACC@1-100Clients | 48.72 | #1 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 10 clients per round) | FedSAM + SWA | ACC@1-100Clients | 46.76 | #2 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 10 clients per round) | FedASAM | ACC@1-100Clients | 46.58 | #3 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 10 clients per round) | FedSAM | ACC@1-100Clients | 44.84 | #4 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 10 clients per round) | FedAvg | ACC@1-100Clients | 41.27 | #5 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 20 clients per round) | FedASAM + SWA | ACC@1-100Clients | 48.27 | #1 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 20 clients per round) | FedASAM | ACC@1-100Clients | 47.78 | #2 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 20 clients per round) | FedSAM + SWA | ACC@1-100Clients | 46.47 | #3 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 20 clients per round) | FedSAM | ACC@1-100Clients | 46.05 | #4 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 20 clients per round) | FedAvg | ACC@1-100Clients | 42.17 | #5 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 5 clients per round) | FedASAM + SWA | ACC@1-100Clients | 49.17 | #1 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 5 clients per round) | FedSAM + SWA | ACC@1-100Clients | 47.96 | #2 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 5 clients per round) | FedASAM | ACC@1-100Clients | 45.61 | #3 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 5 clients per round) | FedSAM | ACC@1-100Clients | 44.73 | #4 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=0.5, 5 clients per round) | FedAvg | ACC@1-100Clients | 40.43 | #5 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 10 clients per round) | FedASAM | ACC@1-100Clients | 54.97 | #1 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 10 clients per round) | FedASAM + SWA | ACC@1-100Clients | 54.79 | #2 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 10 clients per round) | FedSAM + SWA | ACC@1-100Clients | 53.67 | #3 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 10 clients per round) | FedSAM | ACC@1-100Clients | 53.39 | #4 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 10 clients per round) | FedAvg | ACC@1-100Clients | 50.25 | #5 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 20 clients per round) | FedASAM | ACC@1-100Clients | 54.5 | #1 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 20 clients per round) | FedSAM + SWA | ACC@1-100Clients | 54.36 | #2 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 20 clients per round) | FedASAM + SWA | ACC@1-100Clients | 54.1 | #3 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 20 clients per round) | FedSAM | ACC@1-100Clients | 53.97 | #4 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 20 clients per round) | FedAvg | ACC@1-100Clients | 50.66 | #5 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 5 clients per round) | FedASAM | ACC@1-100Clients | 54.81 | #1 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 5 clients per round) | FedSAM | ACC@1-100Clients | 54.01 | #2 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 5 clients per round) | FedSAM + SWA | ACC@1-100Clients | 53.9 | #3 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 5 clients per round) | FedASAM + SWA | ACC@1-100Clients | 53.86 | #4 of 5 | Archive leaderboard | report |
| Federated Learning | CIFAR-100 (alpha=1000, 5 clients per round) | FedAvg | ACC@1-100Clients | 49.92 | #5 of 5 | Archive leaderboard | report |
| Federated Learning | Cityscapes heterogeneous | SiloBN + ASAM | mIoU | 49.75 | #1 of 9 | Archive leaderboard | report |
| Federated Learning | Cityscapes heterogeneous | SiloBN + SAM | mIoU | 49.1 | #2 of 9 | Archive leaderboard | report |
| Federated Learning | Cityscapes heterogeneous | SiloBN | mIoU | 45.96 | #3 of 9 | Archive leaderboard | report |
| Federated Learning | Cityscapes heterogeneous | FedSAM + SWA | mIoU | 43.42 | #4 of 9 | Archive leaderboard | report |
| Federated Learning | Cityscapes heterogeneous | FedASAM + SWA | mIoU | 43.02 | #5 of 9 | Archive leaderboard | report |
| Federated Learning | Cityscapes heterogeneous | FedAvg + SWA | mIoU | 42.48 | #6 of 9 | Archive leaderboard | report |
| Federated Learning | Cityscapes heterogeneous | FedASAM | mIoU | 42.27 | #7 of 9 | Archive leaderboard | report |
| Federated Learning | Cityscapes heterogeneous | FedSAM | mIoU | 41.22 | #8 of 9 | Archive leaderboard | report |
| Federated Learning | Cityscapes heterogeneous | FedAvg | mIoU | 38.65 | #9 of 9 | Archive leaderboard | report |
| Federated Learning | Landmarks-User-160k | FedASAM + SWA | Acc@1-1262Clients | 68.32 | #1 of 6 | Archive leaderboard | report |
| Federated Learning | Landmarks-User-160k | FedSAM + SWA | Acc@1-1262Clients | 68.12 | #2 of 6 | Archive leaderboard | report |
| Federated Learning | Landmarks-User-160k | FedAvg + SWA | Acc@1-1262Clients | 67.52 | #3 of 6 | Archive leaderboard | report |
| Federated Learning | Landmarks-User-160k | FedASAM | Acc@1-1262Clients | 64.23 | #4 of 6 | Archive leaderboard | report |
| Federated Learning | Landmarks-User-160k | FedSAM | Acc@1-1262Clients | 63.72 | #5 of 6 | Archive leaderboard | report |
| Federated Learning | Landmarks-User-160k | FedAvg | Acc@1-1262Clients | 61.91 | #6 of 6 | Archive leaderboard | report |
| Image Classification | CIFAR-100 (alpha=0, 20 clients per round) | FedAvgM + ASAM + SWA | ACC@1-100Clients | 51.58 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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