Secure Distributed Computing Frameworks for AI Model Sharing in Decentralized Environments

Abstract

AI collaboration increasingly spans untrusted, heterogeneous nodes from edge devices to multi-clouds raising acute concerns around privacy, integrity, and verifiability of shared models and updates. This paper proposes a secure distributed computing framework that unifies privacy-preserving learning, verifiable coordination, and incentive-aligned governance for decentralized AI model sharing. The architecture composes federated and peer-to-peer training with secure aggregation, differential privacy, and hardware-backed confidential computing to prevent data leakage while mitigating gradient inversion risks. Model provenance, access control, and policy enforcement are anchored via a lightweight, append-only ledger with decentralized identifiers, enabling auditability without central authorities. To counter poisoning, backdoors, and Sybil attacks, the framework integrates robust aggregation, reputation-weighted participation, and update attestation with zero-knowledge proofs for selective disclosure. A resource-aware scheduler adapts to edge variability using gossip-based dissemination, opportunistic bandwidth utilization, and erasure-coded checkpoints to preserve liveness under churn. Interoperability is ensured through portable model artifacts (e.g., ONNX), secure enclaves for cross-framework execution, and privacy budgets tracked as first-class governance assets. We outline threat models, compliance hooks for jurisdictional constraints, and a token-free contribution accounting mechanism that rewards data quality and validation work. Simulated and real-world deployments illustrate improved end-to-end trust, reduced coordination overhead, and resilient performance under adversarial conditions, positioning the framework as a practical substrate for open, secure, and accountable AI collaboration in decentralized environments