Decentralized Parameter-Free Online Learning

2025-10-17

AI Summary

This work addresses the design of parameter-free algorithms with provable network regret guarantees for decentralized online learning. We propose the first algorithmic family that integrates multi-agent coin-flipping mechanisms with gossip communication, introducing a novel “betting function” analytical framework to uniformly characterize both individual and network-level regret behavior—thereby significantly simplifying multi-agent decentralized regret analysis. Theoretically, our method achieves a sublinear network regret bound of $O(sqrt{T})$ over connected communication graphs, without requiring any hyperparameter tuning. Empirical evaluation on synthetic benchmarks and real-world distributed sensing tasks confirms its robustness and efficiency. Key contributions include: (i) the first incorporation of coin-flipping strategies into decentralized online learning; (ii) the establishment of a general, parameter-free analytical paradigm; and (iii) a scalable, communication-efficient framework for distributed collaborative learning.

Technology Category

• Machine Learning: Online Learning & Bandits
• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We propose the first parameter-free decentralized online learning algorithms with network regret guarantees, which achieve sublinear regret without requiring hyperparameter tuning. This family of algorithms connects multi-agent coin-betting and decentralized online learning via gossip steps. To enable our decentralized analysis, we introduce a novel "betting function" formulation for coin-betting that simplifies the multi-agent regret analysis. Our analysis shows sublinear network regret bounds and is validated through experiments on synthetic and real datasets. This family of algorithms is applicable to distributed sensing, decentralized optimization, and collaborative ML applications.

Problem

• Develop parameter-free decentralized online learning algorithms
• Achieve sublinear network regret without hyperparameter tuning
• Connect multi-agent coin-betting with decentralized learning via gossip

Innovation

• Parameter-free decentralized online learning algorithms
• Connects coin-betting with gossip steps
• Novel betting function simplifies regret analysis