Trustworthy Distributed Machine Learning in Cloud-Edge-IoT Systems: A Heterogeneity-Aware Survey of Security, Privacy, and Robustness

Summary

This survey argues that distributed machine learning, including federated learning, widens the attack surface across cloud-edge-IoT systems and couples privacy, robustness, and fairness challenges. It maps assets, adversaries, and trust assumptions, analyzes how non-IID data and resource asymmetry affect attack detectability and defense reliability, and compares layered defenses such as robust aggregation, differential privacy, secure aggregation, and trusted execution environments. It closes with evaluation and governance guidelines emphasizing reproducible non-IID partitions and auditable lifecycle artifacts.

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