FedGraph: an Aggregation Method from Graph Perspective
Zhifang Deng, Xiaohong Huang, Dandan Li, Xueguang Yuan

TL;DR
FedGraph introduces an adaptive aggregation method for federated learning that considers data distribution, model topology, and training dynamics to improve performance on non-i.i.d datasets.
Contribution
It proposes FedGraph, a novel aggregation strategy that dynamically adjusts weights based on data size, model topology, and training conditions, addressing non-i.i.d data challenges.
Findings
Outperforms state-of-the-art by 2.76 Dice score on FeTS datasets.
Effectively handles non-i.i.d data in federated learning.
Demonstrates improved model correlation understanding through topology graphs.
Abstract
With the increasingly strengthened data privacy act and the difficult data centralization, Federated Learning (FL) has become an effective solution to collaboratively train the model while preserving each client's privacy. FedAvg is a standard aggregation algorithm that makes the proportion of dataset size of each client as aggregation weight. However, it can't deal with non-independent and identically distributed (non-i.i.d) data well because of its fixed aggregation weights and the neglect of data distribution. In this paper, we propose an aggregation strategy that can effectively deal with non-i.i.d dataset, namely FedGraph, which can adjust the aggregation weights adaptively according to the training condition of local models in whole training process. The FedGraph takes three factors into account from coarse to fine: the proportion of each local dataset size, the topology factor of…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Radiomics and Machine Learning in Medical Imaging
