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Decentralized Federated Averaging
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Federated averaging (FedAvg) is a communication efficient algorithm for the distributed training with an enormous number of clients. In FedAvg, clients keep their data locally for privacy protection; a central parameter server is used to communicate between clients. This central server distributes the parameters to each client and collects the updated parameters from clients. FedAvg is mostly studied in centralized fashions, which requires massive communication between server and clients in each communication. Moreover, attacking the central server can break the whole system's privacy. In this paper, we study the decentralized FedAvg with momentum (DFedAvgM), which is implemented on clients that are connected by an undirected graph. In DFedAvgM, all clients perform stochastic gradient descent with momentum and communicate with their neighbors only. To further reduce the communication cost, we also consider the quantized DFedAvgM. We prove convergence of the (quantized) DFedAvgM under trivial assumptions; the convergence rate can be improved when the loss function satisfies the P{\L} property. Finally, we numerically verify the efficacy of DFedAvgM.
Forward citations
Cited by 3 Pith papers
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Reputation-driven Cooperation in Lattice-based Decentralized Federated Learning through Evolutionary Game Theory
In a lattice-based simulation of decentralized federated learning, a reputation mechanism that rewards cooperators and penalizes defectors raises average accuracy from 70% to 82% and drives cooperation to near 100%.
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DFCA: Decentralized Federated Clustering Algorithm
DFCA decentralizes IFCA-style clustered federated learning: clients keep one model per cluster, train their assigned model locally, and exchange only that model with neighbors via a running average, matching centraliz...
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DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models
DP-FedLoRA clips and adds Gaussian noise to per-client LoRA matrices in federated LLM fine-tuning, claiming unbiased updates and bounded variance, but the privacy calibration and experiments have significant gaps.
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