Secure Shapley Value for Cross-Silo Federated Learning
نویسندگان
چکیده
The Shapley value (SV) is a fair and principled metric for contribution evaluation in cross-silo federated learning (cross-silo FL), wherein organizations, i.e., clients, collaboratively train prediction models with the coordination of parameter server. However, existing SV calculation methods FL assume that server can access raw public test data. This may not be valid assumption practice considering emerging privacy attacks on fact data might clients' private assets. Hence, we investigate problem secure FL. We first propose HESV , one-server solution based solely homomorphic encryption (HE) protection, which has limitations efficiency. To overcome these limitations, SecSV an efficient two-server protocol following novel features. First, utilizes hybrid protection scheme to avoid ciphertext-ciphertext multiplications between models, are extremely expensive under HE. Second, matrix multiplication method proposed SecSV. Third, strategically identifies skips some samples without significantly affecting accuracy. Our experiments demonstrate 7.2--36.6× as fast HESV, limited loss accuracy calculated SVs.
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ژورنال
عنوان ژورنال: Proceedings of the VLDB Endowment
سال: 2023
ISSN: ['2150-8097']
DOI: https://doi.org/10.14778/3587136.3587141