Summary of Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients, by Xiaolu Wang et al.
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
by Xiaolu Wang, Zijian Li, Shi Jin, Jun Zhang
First submitted to arxiv on: 17 Feb 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 The proposed Delayed Federated Averaging (DeFedAvg) framework offers an efficient asynchronous federated learning solution for diverse clients with varying computation and communication capabilities. By allowing clients to perform local training with different stale global models at their own paces, DeFedAvg achieves asymptotic convergence rates comparable to those of Federated Averaging (FedAvg) for nonconvex problems. Notably, DeFedAvg is the first algorithm to provably achieve linear speedup, demonstrating its high scalability. Extensive numerical experiments using real-world datasets validate the efficiency and effectiveness of this approach when training deep neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning lets devices learn together without sharing their data. This helps keep information private. In this paper, researchers propose a new way to make this work more efficiently. They want to help devices with different abilities work together better. This means letting each device do its own thing instead of waiting for others to catch up. The new method, called Delayed Federated Averaging, is designed to work quickly and efficiently even when some devices are faster than others. The researchers tested this approach using real-world data and showed that it works well for training artificial intelligence models. |
Keywords
* Artificial intelligence * Federated learning
