Newest papers

Privacy-Preserving Personalized Federated Learning (PPP-FL) for Medical Imaging

Md Morshedul Islam, Hossain Shahriar, Alfredo Cuzzocrea · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27470 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

連邦範囲学習(FL)は、送信コストを軽減し、国境を越えたデータ共有制限を軽減し、ローカルデバイスに原始データを保持することでプライバシーを向上させる分散型機械学習パラダイムである。FLシステムのクライアントは、ローカルモデルを訓練し、中央サーバーによって集計されたアップデートのみを共有します。このセットアップは、直接データ曝露を制限する一方で、FLはプライバシー攻撃に脆弱です。既存の攻撃と防衛戦略は、大規模な医療イメージベースの文脈で広範囲に開発されており、中央システムで原始データを保持することで、FLシステムに適応すると、そのユニークなアーキテクチャとトレーニングワークフロー

Machine translation (Meta M2M100); the English original follows.

Abstract

Federated learning (FL) is a distributed machine learning paradigm that reduces transmission costs, mitigates cross-border data sharing restrictions, and enhances privacy by keeping raw data on local devices. Clients in FL systems train local models and share only updates, which are aggregated by a central server. While this setup limits direct data exposure, FL remains vulnerable to privacy attacks. Existing attack and defense strategies have largely been developed for centralized systems, and adapting them to FL introduces new challenges due to its unique architecture and training workflow. In this work, we first propose a model inversion attack on FL systems using Deep Convolutional Generative Adversarial Networks (DCGANs) in the context of large-scale medical imaging. Our method operates under realistic assumptions about the attacker's knowledge and capabilities. We then examine the effect of client-side differential privacy (DP) in mitigating privacy leakage and incorporate personalized FL to improve model performance under DP settings. Experiments on the Brain MRI ND-5 dataset show that DCGAN-based attacks can reconstruct semantically meaningful synthetic images, revealing significant risks to the privacy of training data. Applying DP reduces attack success but introduces an accuracy trade-off, which is further overcome by personalized FL at each client side. We also evaluate how different aggregation algorithms affect both attack vulnerability and defense effectiveness, underscoring the critical role of aggregation strategies. The proposed attack-and-defense framework is model-agnostic and applicable to a wide range of FL-based systems.

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Topics: Computer science, Federated learning, Differential privacy, Adversarial system, Context (archaeology), Raw data

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