# Personalized and Resilient Distributed Learning Through Opinion Dynamics

arXiv: https://arxiv.org/abs/2505.14081 · HTML: https://mlsanity.com/paper/2505.14081/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 24 of 24)

- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Privacy and Robustness in Federated Learning: Attacks and Defenses](https://mlsanity.com/paper/2012.06337/) (2020)
- [On the Convergence of Decentralized Gradient Descent](https://mlsanity.com/paper/1310.7063/) (2013)
- [Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization](https://mlsanity.com/paper/1709.08765/) (2017)
- [Exact Diffusion for Distributed Optimization and Learning --- Part I: Algorithm Development](https://mlsanity.com/paper/1702.05122/) (2017)
- [BRIDGE: Byzantine-resilient Decentralized Gradient Descent](https://mlsanity.com/paper/1908.08098/) (2019)
- [Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques](https://mlsanity.com/paper/2102.09743/) (2021)
- [Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning](https://mlsanity.com/paper/2202.05318/) (2022)
- [Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learning](https://mlsanity.com/paper/2202.09027/) (2022)
- [Distributed Optimization Under Adversarial Nodes](https://mlsanity.com/paper/1606.08939/) (2016)
- [SplitGP: Achieving Both Generalization and Personalization in Federated Learning](https://mlsanity.com/paper/2212.08343/) (2022)
- [DiNNO: Distributed Neural Network Optimization for Multi-Robot Collaborative Learning](https://mlsanity.com/paper/2109.08665/) (2021)
- [A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part I](https://mlsanity.com/paper/1701.06307/) (2017)
- [Decentralized Personalized Federated Learning: Lower Bounds and Optimal Algorithm for All Personalization Modes](https://mlsanity.com/paper/2107.07190/) (2021)
- [Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey](https://mlsanity.com/paper/2310.10549/) (2023)
- [Robustness and Personalization in Federated Learning: A Unified Approach via Regularization](https://mlsanity.com/paper/2009.06303/) (2020)
- [Distributed Optimization for Smart Cyber-Physical Networks](https://mlsanity.com/paper/1906.10760/) (2019)
- [Distributed and Inexact Proximal Gradient Method for Online Convex Optimization](https://mlsanity.com/paper/2001.00870/) (2020)
- [Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems](https://mlsanity.com/paper/2212.02459/) (2022)
- [A survey on secure decentralized optimization and learning](https://mlsanity.com/paper/2408.08628/) (2024)
- [Can Competition Outperform Collaboration? The Role of Misbehaving Agents](https://mlsanity.com/paper/2207.01346/) (2022)
- [The Role of Confidence for Trust-based Resilient Consensus (Extended Version)](https://mlsanity.com/paper/2404.07838/) (2024)

## Abstract (arXiv)

In this paper, we address two practical challenges of distributed learning in multi-agent network systems, namely personalization and resilience. Personalization is the need of heterogeneous agents to learn local models tailored to their own data and tasks, while still generalizing well; on the other hand, the learning process must be resilient to cyberattacks or anomalous training data to avoid disruption. Motivated by a conceptual affinity between these two requirements, we devise a distributed learning algorithm that combines distributed gradient descent and the Friedkin-Johnsen model of opinion dynamics to fulfill both of them. We quantify its convergence speed and the neighborhood that contains the final learned models, which can be easily controlled by tuning the algorithm parameters to enforce a more personalized/resilient behavior. We numerically showcase the effectiveness of our algorithm on synthetic and real-world distributed learning tasks, where it achieves high global accuracy both for personalized models and with malicious agents compared to standard strategies.