Momentum Approximation in Asynchronous Private Federated Learning
This paper was accepted for presentation at the International Workshop on Federated Foundation Models (FL@FM-NeurIPS’24), held in conjunction with NeurIPS 2024. Asynchronous protocols have been shown to improve the scalability of federated learning (FL) with a massive number of clients. Meanwhile, momentum-based methods can achieve the best model quality in synchronous FL. However, naively applying …
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