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Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
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We investigate the optimization aspects of personalized Federated Learning (FL). We propose general optimizers that can be applied to numerous existing personalized FL objectives, specifically a tailored variant of Local SGD and variants of accelerated coordinate descent/accelerated SVRCD. By examining a general personalized objective capable of recovering many existing personalized FL objectives as special cases, we develop a comprehensive optimization theory applicable to a wide range of strongly convex personalized FL models in the literature. We showcase the practicality and/or optimality of our methods in terms of communication and local computation. Remarkably, our general optimization solvers and theory can recover the best-known communication and computation guarantees for addressing specific personalized FL objectives. Consequently, our proposed methods can serve as universal optimizers, rendering the design of task-specific optimizers unnecessary in many instances.
Forward citations
Cited by 3 Pith papers
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PFedDST: Personalized Federated Learning with Decentralized Selection Training
A decentralized personalized federated learning method that scores peers by loss, header similarity, and recency reports faster convergence, but its own CIFAR-100 result contradicts the accuracy claim.
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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization
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