Summary of Efficient Adaptive Federated Optimization, by Su Hyeong Lee and Sidharth Sharma and Manzil Zaheer and Tian Li
Efficient Adaptive Federated Optimization
by Su Hyeong Lee, Sidharth Sharma, Manzil Zaheer, Tian Li
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Optimization and Control (math.OC)
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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 paper introduces a new class of efficient adaptive algorithms, named FedAda^2 and its enhanced version FedAda^2++, designed specifically for large-scale, cross-device federated environments. The algorithms optimize communication efficiency by avoiding the transfer of preconditioners between the server and clients, while also incorporating memory-efficient adaptive optimizers on the client side. This results in reduced on-device memory usage without sacrificing convergence rates for general, non-convex objectives. Extensive empirical evaluations demonstrate the effectiveness of FedAda2/FedAda2++. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates new algorithms to help machines learn together more efficiently. It shows how to make communication and memory use more efficient in federated learning, a method that lets devices share knowledge without sharing their data. The algorithm is tested on pictures and text datasets and performs well. |
Keywords
» Artificial intelligence » Federated learning
