Problem
Research questions and friction points this paper is trying to address.
Federated Learning
Voting-based FL
Class-disjoint Non-IID Data
Certified Accuracy
CARVY-FL: Client Anticlustering for Robust Voting in Provably Secure Federated Learning
🤖 AI Summary
本文提出CARVY-FL方法，通过估计客户端数据分布并采用反聚类提高组内多样性，以增强联邦学习在恶意客户端存在下的鲁棒性。
📝 Abstract
Federated learning (FL) enables collaborative training without directly sharing raw data, but remains vulnerable to malicious clients. Voting-based FL improves robustness by partitioning clients into groups, training one model per group, and aggregating predictions by plurality voting. However, under class-disjoint non-IID data, distribution-oblivious grouping can yield highly variable certified accuracy (CA). We propose CARVY-FL, which estimates client distribution types from one-epoch model updates and uses anticlustering to increase within-group distributional diversity. Under a fixed grouping, CARVY-FL retains the voting-based CA guarantee while increasing vote margins. Experiments on MNIST and Fashion-MNIST show higher CA than FLCert. Under BadNets with model replacement, CARVY-FL improves the AUC of 100-ASR by 11.1% and 14.9%, respectively.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Client Anticlustering
Distributional Diversity
Voting-based CA Guarantee
Federated Learning
Robustness
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