Privacy-Preserving and Verifiable Approximate Distributed Coded Computing
Distributed machine learning enables collaborative model training without centralizing data, but it also exposes learning processes to privacy leakage and malicious manipulation. Existing defenses typically address these threats in isolation and are often tailored to specific learning paradigms or model architectures, limiting their applicability in realistic deployments. In particular, federated learning and decentralized learning exhibit distinct adversarial surfaces that are rarely addressed within a unified framework. In this paper, we present a model-agnostic framework for adversary-resis
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- PossiblePossibly related (embedding) · 46%Mesh-LLM/mesh-llm →
- PossiblePossibly related (embedding) · 46%opensandbox-group/OpenSandbox →
- PossiblePossibly related (embedding) · 46%SentryCode: Real-time Auditor + Honeytokens for AI Coding Agents [P] →
- LinkedLinked via arxiv author · 85%Xavier Martínez-Luaña →
“Privacy-Preserving and Verifiable Approximate Distributed Coded Computing”
- LinkedLinked via arxiv author · 85%Alba Gude-Santos →
“Privacy-Preserving and Verifiable Approximate Distributed Coded Computing”
- LinkedLinked via arxiv author · 85%Manuel Fernández-Veiga →
“Privacy-Preserving and Verifiable Approximate Distributed Coded Computing”
- LinkedLinked via arxiv author · 85%Rebeca P. Díaz-Redondo →
“Privacy-Preserving and Verifiable Approximate Distributed Coded Computing”
- PossiblePossibly related (embedding) · 51%Behavioral Privacy Leakage in Agentic Negotiation: Formalizing and Mitigating Inference Attacks via Randomized Policies - Apple Machine Learning Research →