Mixed Federated Learning: Joint Decentralized and Centralized Learning

Sean Augenstein, Andrew Hard, Lin Ning, Karan Singhal, Satyen Kale, Kurt Partridge, Rajiv Mathews

Keywords: federated learning, decentralized learning, privacy, security, distribution shift, distribution skew, mobile computing

TL;DR: Federated learning (FL) is good (better privacy, higher accuracy), and 'mixed FL' (concurrent joint FL and centralized learning) can make it even better, by mitigating distribution shifts and saving bandwidth and compute.

Abstract: Federated learning (FL) enables learning from decentralized privacy-sensitive data, with computations on raw data confined to take place at edge clients. This paper introduces mixed FL, which incorporates an additional loss term calculated at the coordinating server (while maintaining FL’s private data restrictions). For example, additional datacenter data can be leveraged to jointly learn from centralized (datacenter) and decentralized (federated) training data and better match an expected inference data distribution.Mixed FL also enables offloading some intensive computations (e.g., embedding regularization) to the server, greatly reducing communication and client computation load. For these and other mixed FL use cases, we present three algorithms: PARALLEL TRAINING, 1-WAY GRADIENT TRANSFER, and 2-WAY GRADIENT TRANSFER. We perform extensive experiments of the algorithms on three tasks, demonstrating that mixed FL can blend training data to achieve an oracle’s accuracy on an inference distribution, and can reduce communication and computation overhead by more than 90%. Finally, we state convergence bounds for all algorithms, and give intuition on the mixed FL problems best suited to each. The theory confirms our empirical observations of how the algorithms perform under different mixed FL problem settings.

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