SphereFed: Hyperspherical Federated Learning

• URL: http://arxiv.org/abs/2207.09413v1
• Date: Tue, 19 Jul 2022 17:13:06 GMT
• Title: SphereFed: Hyperspherical Federated Learning
• Authors: Xin Dong, Sai Qian Zhang, Ang Li, H.T. Kung
• Abstract summary: Key challenge is the handling of non-i.i.d. data across multiple clients. We introduce the Hyperspherical Federated Learning (SphereFed) framework to address the non-i.i.d. issue. We show that the calibration solution can be computed efficiently and distributedly without direct access of local data.
• Score: 22.81101040608304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning aims at training a global model from multiple decentralized devices (i.e. clients) without exchanging their private local data. A key challenge is the handling of non-i.i.d. (independent identically distributed) data across multiple clients that may induce disparities of their local features. We introduce the Hyperspherical Federated Learning (SphereFed) framework to address the non-i.i.d. issue by constraining learned representations of data points to be on a unit hypersphere shared by clients. Specifically, all clients learn their local representations by minimizing the loss with respect to a fixed classifier whose weights span the unit hypersphere. After federated training in improving the global model, this classifier is further calibrated with a closed-form solution by minimizing a mean squared loss. We show that the calibration solution can be computed efficiently and distributedly without direct access of local data. Extensive experiments indicate that our SphereFed approach is able to improve the accuracy of multiple existing federated learning algorithms by a considerable margin (up to 6% on challenging datasets) with enhanced computation and communication efficiency across datasets and model architectures.

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