FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching 事件

FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching arXiv:2606.10124v1 Announce Type: cross Abstract: Federated learning (FL) is often subject to aggregation variance if clients do not consistently participate in training rounds. While reusing stale model updates from inactive clients is a common technique to reduce this variance, we find that with skewed client participation, the resulting update staleness can become severe enough to destab