Summary of Fedstaleweight: Buffered Asynchronous Federated Learning with Fair Aggregation Via Staleness Reweighting, by Jeffrey Ma et al.
FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
by Jeffrey Ma, Alan Tu, Yiling Chen, Vijay Janapa Reddi
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A Federated Learning (FL) algorithm called FedStaleWeight has been introduced to address fairness issues in Asynchronous Federated Learning (AFL). AFL methods aim to improve the scalability and performance of FL by allowing clients to update models at different rates. However, this can lead to biases towards faster updating agents and slower agents being left behind. The proposed algorithm uses average staleness to compute fair re-weightings, reframing asynchronous federated learning aggregation as a mechanism design problem. This approach incentivizes truthful compute speed reporting without favoring faster update-producing agents. FedStaleWeight provides theoretical convergence guarantees in the smooth, non-convex setting and empirically outperforms the commonly used asynchronous FedBuff with gradient averaging, achieving stronger fairness and expediting convergence to a higher global model accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of sharing information between devices while keeping data private is called Federated Learning (FL). FL is important because it helps protect personal data. There are challenges in making this work well, like speed and collaboration. Some methods try to fix these problems by not waiting for all the devices to share their updates at once. However, this can create other issues, like some devices being left behind or sharing false information. A new algorithm called FedStaleWeight helps solve these fairness problems by using how long it takes each device to share its update as a guide. This makes sure that each device has an equal chance of influencing the overall model. The results show that FedStaleWeight is better than other methods at achieving fairness and accuracy. |
Keywords
» Artificial intelligence » Federated learning
