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

Hindi

Federated Range Learning (FL) एक वितरित मशीन लर्निंग पैराग्रामिम है जो ट्रांसमिशन लागत को कम करता है, सीमा पार डेटा साझा करने की प्रतिबंधों को कम करता है, और स्थानीय उपकरणों पर कस्टम डेटा रखने के द्वारा गोपनीयता को बढ़ाता है। FL सिस्टम में ग्राहक स्थानीय मॉडल प्रशिक्षित करते हैं और केवल अपडेट साझा करते हैं, जो एक केंद्रीय सर्वर द्वारा एकत्र किए जाते हैं। जबकि यह सेटिंग सीधे डेटा के संपर्क को सीमित करता है, FL गोपनीयता के हमलों के संदर्भ में संवेदनशील रहता है। मौजूदा हमले और रक्षा रणनीतियां बड़े पैमाने पर चिकित्सा छवियों के संदर्भ में विकसित की गई हैं, और FL के लिए उनका अनुकूलन उनके अद्वितीय आर्किटे

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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