A Unified Framework for Fair and Personalized Decentralized Learning under Communication Constraints
Proposes DMFL-SQ, a decentralized multi-task learning algorithm that jointly addresses personalization, fairness, and communication efficiency in distributed learning systems Introduces a unified framework combining graph-based personalization, agnostic mixture fairness objectives, and compressed event-triggered communication via sparsification and quantization Proves O(T^(-1/2)) convergence rate in expected squared Moreau-envelope stationarity for general non-convex objectives, matching the dom
Analysis
TL;DR
- Proposes DMFL-SQ, a decentralized multi-task learning algorithm that jointly addresses personalization, fairness, and communication efficiency in distributed learning systems
- Introduces a unified framework combining graph-based personalization, agnostic mixture fairness objectives, and compressed event-triggered communication via sparsification and quantization
- Proves O(T^(-1/2)) convergence rate in expected squared Moreau-envelope stationarity for general non-convex objectives, matching the dominant rate despite communication compression
- Derives PAC-Bayes generalization guarantees for the fairness-aware mixture objective, providing theoretical bounds on out-of-distribution performance
- Empirical validation on CIFAR-10 and the heterogeneous MUSMET EEG dataset demonstrates substantial communication reduction while improving cross-client fairness and maintaining predictive accuracy
Why It Matters
This work directly addresses a critical tension in decentralized ML: the trade-off between communication efficiency and fairness across heterogeneous clients. For practitioners building federated or peer-to-peer learning systems, it provides both theoretical guarantees and practical algorithms showing that fairness and communication compression need not come at the cost of convergence speed. The results are particularly relevant for resource-constrained edge computing and privacy-sensitive applications where both bandwidth and equitable performance are paramount.
Technical Details
- Algorithm (DMFL-SQ): A decentralized multi-task learning method that performs personalized model training over a communication graph while optimizing an agnostic mixture fairness objective. Communication is reduced through three complementary techniques: gradient sparsification, quantization, and event-triggered synchronization that only broadcasts updates when significant changes occur.
- Convergence Analysis: Establishes convergence guarantees for general non-convex objectives, proving an O(T^(-1/2)) rate in expected squared Moreau-envelope stationarity. This rate is preserved despite the combined effects of sparsity, quantization noise, and infrequent event-triggered communication rounds.
- Generalization Bounds: Derives PAC-Bayes generalization guarantees tailored to the fairness-aware mixture objective, linking the empirical fairness performance to theoretical bounds on generalization error across clients with heterogeneous data distributions.
- Datasets and Benchmarks: Evaluated on CIFAR-10 (standard benchmark with simulated non-IID splits) and the MUSMET EEG dataset (real-world heterogeneous medical data with significant inter-subject variability), demonstrating both communication savings and fairness improvements over baseline decentralized methods.
Industry Insight
- The joint optimization of fairness and communication efficiency challenges the common assumption that these goals are inherently conflicting; practitioners should consider unified frameworks rather than treating fairness as a post-hoc adjustment in decentralized deployments.
- Event-triggered communication combined with sparsification and quantization offers a practical blueprint for deploying decentralized learning on bandwidth-constrained edge devices, potentially reducing communication costs by orders of magnitude without sacrificing model quality.
- The PAC-Bayes fairness generalization bounds provide a novel theoretical tool for auditing decentralized systems; organizations deploying client-level ML should adopt similar fairness-aware generalization analysis to ensure equitable performance across diverse user populations.
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