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First Analysis of Local GD on Heterogeneous Data
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We provide the first convergence analysis of local gradient descent for minimizing the average of smooth and convex but otherwise arbitrary functions. Problems of this form and local gradient descent as a solution method are of importance in federated learning, where each function is based on private data stored by a user on a mobile device, and the data of different users can be arbitrarily heterogeneous. We show that in a low accuracy regime, the method has the same communication complexity as gradient descent.
Forward citations
Cited by 3 Pith papers
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FedWSQ: Efficient Federated Learning with Weight Standardization and Distribution-Aware Non-Uniform Quantization
FedWSQ applies weight standardization in federated learning and uses Gaussian-optimal non-uniform quantization with a shared global scaling vector, improving accuracy at very low bit rates.
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Optimization Methods and Software for Federated Learning
A thesis that packages the author's published federated learning work, whose main new theoretical result is an improved complexity bound for error-feedback compression.
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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization
A PhD dissertation showing unified compression theory, personalized accelerated local training, and pruning methods that reduce communication costs in federated learning and maintain accuracy in LLM pruning.
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