Personalized and Resilient Distributed Learning Through Opinion Dynamics

2025-05-20

AI Summary

This paper addresses the challenge of simultaneously achieving personalized modeling and adversarial robustness in multi-agent distributed learning. We propose a novel framework that integrates distributed gradient descent with Friedkin-Johnsen opinion dynamics—the first such incorporation of social opinion evolution mechanisms into distributed optimization. Our approach unifies two objectives: (i) personalized adaptation of each agent to its local task, and (ii) robust consensus against malicious agents or anomalous data. A tunable parameter enables flexible trade-offs between personalization accuracy and system resilience. We establish theoretical convergence guarantees under standard assumptions. Empirical evaluation on synthetic and real-world datasets demonstrates that our method achieves higher global accuracy than baseline approaches and maintains stable performance under adversarial conditions—including scenarios with malicious agents—thereby significantly enhancing the practicality and reliability of distributed learning systems.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Distributed Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Addresses personalization in distributed learning for heterogeneous agents
• Ensures resilience against cyberattacks and anomalous data
• Combines gradient descent and opinion dynamics for robust learning

Innovation

• Combines gradient descent with opinion dynamics
• Ensures personalized resilient distributed learning
• Controls convergence via tunable parameters