# Privacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Framework

arXiv: https://arxiv.org/abs/2605.09232 · HTML: https://mlsanity.com/paper/2605.09232/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 10 May 2026.

## Builds on (most cited first, 7 of 7)

- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Collecting and Analyzing Multidimensional Data with Local Differential Privacy](https://mlsanity.com/paper/1907.00782/) (2019)
- [6G Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2108.04973/) (2021)
- [Privacy-Preserving Aggregation in Federated Learning: A Survey](https://mlsanity.com/paper/2203.17005/) (2022)
- [Management of Resource at the Network Edge for Federated Learning](https://mlsanity.com/paper/2107.03428/) (2021)
- [Bloom Filter Encoding for Machine Learning](https://mlsanity.com/paper/2512.19991/) (2025)

## Abstract (arXiv)

The increasing deployment of Internet-of-Things (IoT) devices has accelerated the use of distributed learning frameworks, where data remains local while model updates are shared across decentralized systems. Although this reduces centralized data collection, it introduces privacy risks through the exchange of gradients, model parameters, and intermediate representations. A variety of privacy-preserving techniques have been proposed to address these risks, including differential privacy, cryptographic methods, and lightweight system-level approaches. However, existing surveys often evaluate these methods in isolation and lack a unified framework for comparing their effectiveness under realistic attack models and IoT resource constraints. This paper presents a structured analysis of privacy-preserving techniques for distributed learning in IoT environments. A unified threat model is introduced that captures model inversion, membership inference, gradient leakage, and communication-based attacks. Building on this model, an evaluation framework is developed to compare methods in terms of both privacy robustness and system-level efficiency, including computational, memory, and communication overhead. Using this framework, representative approaches including differential privacy, homomorphic encryption, secure multi-party computation, distributed selective stochastic gradient descent, and Bloom Filter-based methods are analyzed. The results highlight a fundamental trade-off between privacy strength and system efficiency. In particular, Bloom Filter-based encodings are shown to provide lightweight privacy through collision-induced ambiguity while maintaining low computational and communication overhead. The paper provides a unified perspective on privacy-preserving design choices for distributed learning in IoT systems.