Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap

TL;DR

This survey comprehensively analyzes privacy-preserving machine learning techniques tailored for IoT, addressing challenges, paradigms, and future research directions in decentralized, resource-constrained environments.

Contribution

It introduces a structured taxonomy of privacy-preserving methods for IoT, covering formal guarantees, complexity, scalability, and threat resilience, along with deployment insights and open challenges.

Findings

Differential privacy and federated learning are key paradigms for IoT privacy.

Trade-offs exist between privacy, communication overhead, and model accuracy.

Open challenges include hybrid approaches, energy efficiency, and quantum resilience.

Abstract

The rapid proliferation of the Internet of Things has intensified demand for robust privacy-preserving machine learning mechanisms to safeguard sensitive data generated by large-scale, heterogeneous, and resource-constrained devices. Unlike centralized environments, IoT ecosystems are inherently decentralized, bandwidth-limited, and latency-sensitive, exposing privacy risks across sensing, communication, and distributed training pipelines. These characteristics render conventional anonymization and centralized protection strategies insufficient for practical deployments. This survey presents a comprehensive IoT-centric, cross-paradigm analysis of privacy-preserving machine learning. We introduce a structured taxonomy spanning perturbation-based mechanisms such as differential privacy, distributed paradigms such as federated learning, cryptographic approaches including homomorphic encryption and secure multiparty computation, and generative synthesis techniques based on generative adversarial networks. For each paradigm, we examine formal privacy guarantees, computational and communication complexity, scalability under heterogeneous device participation, and resilience against threats including membership inference, model inversion, gradient leakage, and adversarial manipulation. We further analyze deployment constraints in wireless IoT environments, highlighting trade-offs between privacy, communication overhead, model convergence, and system efficiency within next-generation mobile architectures. We also consolidate evaluation methodologies, summarize representative datasets and open-source frameworks, and identify open challenges including hybrid privacy integration, energy-aware learning, privacy-preserving large language models, and quantum-resilient machine learning.