# The Internet of Things: Secure Distributed Inference

arXiv: https://arxiv.org/abs/1806.09199 · HTML: https://mlsanity.com/paper/1806.09199/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2018.

## Built on by (most cited first, 9 of 9)

- [BROADCAST: Reducing Both Stochastic and Compression Noise to Robustify Communication-Efficient Federated Learning](https://mlsanity.com/paper/2104.06685/) (2021)
- [Robust Distributed Optimization With Randomly Corrupted Gradients](https://mlsanity.com/paper/2106.14956/) (2021)
- [Robust Federated Learning via Over-The-Air Computation](https://mlsanity.com/paper/2111.01221/) (2021)
- [Federated Multi-Armed Bandits Under Byzantine Attacks](https://mlsanity.com/paper/2205.04134/) (2022)
- [Over-The-Air Federated Learning under Byzantine Attacks](https://mlsanity.com/paper/2205.02949/) (2022)
- [Stochastic Alternating Direction Method of Multipliers for Byzantine-Robust Distributed Learning](https://mlsanity.com/paper/2106.06891/) (2021)
- [Byzantine-Robust Federated Learning via Credibility Assessment on Non-IID Data](https://mlsanity.com/paper/2109.02396/) (2021)
- [Design and Analysis of Robust Resilient Diffusion over Multi-Task Networks Against Byzantine Attacks](https://mlsanity.com/paper/2206.12749/) (2022)
- [Spatially-resolved hyperlocal weather prediction and anomaly detection using IoT sensor networks and machine learning techniques](https://mlsanity.com/paper/2310.11001/) (2023)

## Abstract (arXiv)

The growth in the number of devices connected to the Internet of Things (IoT) poses major challenges in security. The integrity and trustworthiness of data and data analytics are increasingly important concerns in IoT applications. These are compounded by the highly distributed nature of IoT devices, making it infeasible to prevent attacks and intrusions on all data sources. Adversaries may hijack devices and compromise their data. As a result, reactive countermeasures, such as intrusion detection and resilient analytics, become vital components of security. This paper overviews algorithms for secure distributed inference in IoT.