A Unifying Framework for Federated Learning

There have been multiple federated learning (FL) algorithms proposed in the FL community during the recent years. However, a thorough comparison of these algorithms has not been done, and our understanding of the theory of FL is still limited. The lack of a unifying view in practice has also led to the reinvention of the same algorithms under different names. Motivated by this gap, we develop a unifying scheme for FL and demonstrate that many of the algorithms that exist in the FL literature are special cases of this scheme. The unification allows us to get a deeper understanding of different FL algorithms, to compare them easier, to improve the previous results for their convergence analysis and to find new FL algorithms. In particular, we demonstrate the important role that step size plays in the convergence of FL algorithms. Further, based on our unifying scheme, we propose an efficient and economic method for accelerating FL algorithms. This streamlined acceleration method does not incur any communication overheads. We evaluate our findings by performing extensive experiments on both nonconvex and convex problems.

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