Privacy Enhancing Cross-Silo Federated Learning For FDIA Using ML
DOI:
https://doi.org/10.48001/978-81-966500-7-0-8Keywords:
False data injection attack, Cross-Silo, Shamir’s Secrete Sharing, Privacy Preserve, Federated LearningAbstract
Combined Learning (CL) tackles data privacy by allowing users to store data locally and share only model parameters with a central server to train a global model. However, CL is vulnerable to inference attacks from untrusted aggregators. Existing solutions often require a trusted third party or inefficient protocols. Our work proposes an efficient privacy-preserving federated learning scheme with strong security. By designing a dual-layer encryption scheme without the need for discrete logarithm calculations, using secret sharing only initially and when groups rejoin, and enhancing computational efficiency through parallel processing, we ensure secure federated learning. This method is applied to detect false data injection attacks (FDIA) in smart systems, offering improved security and resistance to private data inference attacks compared to previous methods.
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