# Online Decentralized Federated Multi-task Learning With Trustworthiness in Cyber-Physical Systems

arXiv: https://arxiv.org/abs/2509.00992 · HTML: https://mlsanity.com/paper/2509.00992/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 32 references that are in the map.
- First posted on arXiv 31 Aug 2025.

## Builds on (most cited first, 22 of 22)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Federated learning with hierarchical clustering of local updates to improve training on non-IID data](https://mlsanity.com/paper/2004.11791/) (2020)
- [Federated Multi-Task Learning under a Mixture of Distributions](https://mlsanity.com/paper/2108.10252/) (2021)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Recovering Private Text in Federated Learning of Language Models](https://mlsanity.com/paper/2205.08514/) (2022)
- [Variational Federated Multi-Task Learning](https://mlsanity.com/paper/1906.06268/) (2019)
- [Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning](https://mlsanity.com/paper/2112.14468/) (2021)
- [BRIDGE: Byzantine-resilient Decentralized Gradient Descent](https://mlsanity.com/paper/1908.08098/) (2019)
- [Geometric Median in Nearly Linear Time](https://mlsanity.com/paper/1606.05225/) (2016)
- [ByRDiE: Byzantine-resilient distributed coordinate descent for decentralized learning](https://mlsanity.com/paper/1708.08155/) (2017)
- [Byzantine-resilient Decentralized Stochastic Gradient Descent](https://mlsanity.com/paper/2002.08569/) (2020)
- [Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2102.03401/) (2021)
- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [Proximity Without Consensus in Online Multi-Agent Optimization](https://mlsanity.com/paper/1606.05578/) (2016)
- [Dynamic Federated Learning](https://mlsanity.com/paper/2002.08782/) (2020)
- [CP-mtML: Coupled Projection multi-task Metric Learning for Large Scale Face Retrieval](https://mlsanity.com/paper/1604.02975/) (2016)
- [Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems](https://mlsanity.com/paper/2212.02459/) (2022)
- [Characterizing Trust and Resilience in Distributed Consensus for Cyberphysical Systems](https://mlsanity.com/paper/2103.05464/) (2021)
- [Decentralized Multi-Task Online Convex Optimization Under Random Link Failures](https://mlsanity.com/paper/2401.02011/) (2024)

## Abstract (arXiv)

Multi-task learning is an effective way to address the challenge of model personalization caused by high data heterogeneity in federated learning. However, extending multi-task learning to the online decentralized federated learning setting is yet to be explored. The online decentralized federated learning setting considers many real-world applications of federated learning, such as autonomous systems, where clients communicate peer-to-peer and the data distribution of each client is time-varying. A more serious problem in real-world applications of federated learning is the presence of Byzantine clients. Byzantine-resilient approaches used in federated learning work only when the number of Byzantine clients is less than one-half the total number of clients. Yet, it is difficult to put a limit on the number of Byzantine clients within a system in reality. However, recent work in robotics shows that it is possible to exploit cyber-physical properties of a system to predict clients' behavior and assign a trust probability to received signals. This can help to achieve resiliency in the presence of a dominating number of Byzantine clients. Therefore, in this paper, we develop an online decentralized federated multi-task learning algorithm to provide model personalization and resiliency when the number of Byzantine clients dominates the number of honest clients. Our proposed algorithm leverages cyber-physical properties, such as the received signal strength in wireless systems or side information, to assign a trust probability to local models received from neighbors in each iteration. Our simulation results show that the proposed algorithm performs close to a Byzantine-free setting.