Cooperative Gradient Coding

2025-07-07

AI Summary

To address the convergence challenges of gradient coding (GC) under unreliable communication—caused by redundant data replication and rigid decoding—we propose CoGC, a collaborative GC framework, and GC⁺, an enhanced decoding mechanism. CoGC eliminates the need for global dataset replication by enabling inter-client coordination, thereby improving both communication and computational efficiency. GC⁺ relaxes conventional binary decoding constraints, enabling partial gradient reuse to significantly enhance fault tolerance and convergence stability during channel outages. Theoretical analysis establishes convergence bounds for both mechanisms, with performance limits derived via outage probability modeling and GC matrix analysis. Experiments demonstrate that CoGC reduces communication overhead, while GC⁺ improves training success rate by over 40% under severe channel conditions. Together, CoGC and GC⁺ substantially improve the robustness and practicality of federated learning systems in unreliable network environments.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This work studies gradient coding (GC) in the context of distributed training problems with unreliable communication. We propose cooperative GC (CoGC), a novel gradient-sharing-based GC framework that leverages cooperative communication among clients. This approach ultimately eliminates the need for dataset replication, making it both communication- and computation-efficient and suitable for federated learning (FL). By employing the standard GC decoding mechanism, CoGC yields strictly binary outcomes: either the global model is exactly recovered, or the decoding fails entirely, with no intermediate results. This characteristic ensures the optimality of the training and demonstrates strong resilience to client-to-server communication failures when the communication channels among clients are in good condition. However, it may also result in communication inefficiency and hinder convergence due to its lack of flexibility, especially when communication channels among clients are in poor condition. To overcome this limitation and further harness the potential of GC matrices, we propose a complementary decoding mechanism, termed GC$^+$, which leverages information that would otherwise be discarded during GC decoding failures. This approach significantly improves system reliability under unreliable communication, as the full recovery of the global model typically dominates in GC$^+$. To conclude, this work establishes solid theoretical frameworks for both CoGC and GC$^+$. We provide complete outage analyses for each decoding mechanism, along with a rigorous investigation of how outages affect the structure and performance of GC matrices. Building on these analyses, we derive convergence bounds for both decoding mechanisms. Finally, the effectiveness of CoGC and GC$^+$ is validated through extensive simulations.

Problem

• Enhances gradient coding for unreliable distributed training communication
• Eliminates dataset replication for efficient federated learning
• Improves resilience with cooperative client communication and GC+ decoding

Innovation

• Cooperative GC framework for efficient federated learning
• Binary GC decoding ensures optimal training resilience
• GC+ decoding improves reliability under poor communication