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Secure Byzantine-Robust Machine Learning
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Increasingly machine learning systems are being deployed to edge servers and devices (e.g. mobile phones) and trained in a collaborative manner. Such distributed/federated/decentralized training raises a number of concerns about the robustness, privacy, and security of the procedure. While extensive work has been done in tackling with robustness, privacy, or security individually, their combination has rarely been studied. In this paper, we propose a secure two-server protocol that offers both input privacy and Byzantine-robustness. In addition, this protocol is communication-efficient, fault-tolerant and enjoys local differential privacy.
Forward citations
Cited by 2 Pith papers
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DSFL: A Dual-Server Byzantine-Resilient Federated Learning Framework via Group-Based Secure Aggregation
DSFL claims a lightweight dual-server federated learning protocol that hides updates with additive noise and filters Byzantine clients using group deviation scores.
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Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning
ByITFL and LoByITFL are Byzantine-resilient federated learning schemes that claim perfect information-theoretic privacy using a polynomial discriminator and secret sharing, but the privacy proof has a gap around per-u...
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