FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning

Problem
This paper addresses the challenge of modeling label correlations in Federated Multi-Label Learning (FML) where clients possess heterogeneous multi-label data. The authors identify a significant gap in the literature regarding the phenomenon of label correlation drift, which occurs due to client-specific label spaces and varying co-occurrence patterns. This drift leads to biased local estimates of label correlations that deviate from a global structure. The work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution is the FedHarmony framework, which harmonizes heterogeneous label correlations across clients. FedHarmony introduces a consensus correlation mechanism that captures the agreement among clients, acting as a global teacher to correct local biases. The framework employs a weighted aggregation strategy, where clients are evaluated based on both the size of their data and the quality of their label correlations, allowing for adaptive weighting during the aggregation process. Additionally, the authors propose an accelerated optimization algorithm for FedHarmony, which theoretically guarantees faster convergence rates without compromising accuracy. The specifics of the architecture, loss functions, and training compute are not disclosed in the abstract.

Results
FedHarmony demonstrates superior performance on real-world federated multi-label datasets compared to state-of-the-art methods. The paper reports significant improvements in metrics such as F1-score and Hamming loss, although specific numerical results and baseline comparisons are not detailed in the abstract. The authors claim that their method consistently outperforms existing approaches, indicating a robust effect size in the context of federated learning.

Limitations
The authors acknowledge that their approach may still be sensitive to the heterogeneity of client data distributions, which could affect the quality of the consensus correlation. They do not address potential scalability issues when the number of clients increases significantly or the computational overhead introduced by the accelerated optimization algorithm. Additionally, the impact of varying communication rounds and the potential for communication bottlenecks in federated settings are not discussed.

Why it matters
The implications of this work are significant for advancing federated learning methodologies, particularly in scenarios where data privacy is paramount. By effectively addressing label correlation drift, FedHarmony enhances the reliability of multi-label predictions in distributed environments, paving the way for more accurate collaborative learning across heterogeneous data sources. This framework could facilitate applications in domains such as healthcare, finance, and social media, where multi-label classification is prevalent and data privacy concerns are critical.

Authors: Zhiqiang Kou, Junxiang Wu, Wenke Huang, Wenwen He, Ming-Kun Xie, Changwei Wang, Yuheng Jia, Di Jiang et al.
Source: arXiv:2604.28024
URL: https://arxiv.org/abs/2604.28024v1