Dec 4, 2023 · Our novel aggregation method, FedBayes, mitigates the effect of a malicious client by calculating the probabilities of a client's model weights.
Abstract—Federated learning has created a decentralized method to train a machine learning model without needing direct access to client data.
In FEDBAYES [341] , the authors propose using Bayesian statistics to calculate the probabilities of clients' model weights and use them to identify potential ...
In this paper, we propose an enhanced Membership Inference Attack with the Batch-wise generated Attack Dataset (MIA-BAD), a modification to the MIA approach.
Sep 7, 2024 · 當前主要的聚合方法是FedAvg的變體，將每個客戶端的權重平均在一起形成全局模型。然而，這些聚合方法特別容易受到對抗性攻擊，如backdoor attacks、label ...
Dec 4, 2023 · FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks. Federated learning has created a decentralized ...
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks ... Federated learning has created a decentralized method to ...
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Dec 10, 2023 · Excited to share our latest blog post on "FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks"!
Aug 25, 2024 · FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks. CCWC 2024: 28-35. [c3]. view. electronic edition ...
2022. FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks. M Vucovich, D Quinn, K Choi, C Redino, A Rahman, E Bowen.