Md Morshedul Islam, Hossain Shahriar, Alfredo Cuzzocrea · 2026 · arXiv
DOI: 10.48550/arXiv.2610.27470 · open-access PDF
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.
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