提出联邦学习中多目标优化的系统分类框架，解决公平性与效率冲突问题。
Multi-objective methods in Federated Learning: A survey and taxonomy
- 构建首个联邦学习与多目标优化融合的分类体系
- 梳理现有方法并标注清晰的技术标签
- 适合关注分布式机器学习公平性研究者参考
联邦学习在训练数据分散于多个客户端的场景下实现了高效的分布式机器学习。随着该策略普及，现实问题日益复杂，常需平衡公平性、性能和资源消耗等相互冲突的目标。近期研究开始引入多目标视角应对挑战，但联邦学习与多目标优化的结合尚未在两领域整体背景下被系统探讨。本文首次提出一个清晰、系统的整合框架，建立首个关于联邦学习中多目标方法的分类体系，全面综述最新进展，并为各类贡献提供明确标签。该分类体系具有前瞻性，既涵盖现有工作，也支持未来扩展。最后，指出当前开放挑战与潜在研究方向。
原文摘要 · Abstract (English)
The Federated Learning paradigm facilitates effective distributed machine learning in settings where training data is decentralized across multiple clients. As the popularity of the strategy grows, increasingly complex real-world problems emerge, many of which require balancing conflicting demands such as fairness, utility, and resource consumption. Recent works have begun to recognise the use of a multi-objective perspective in answer to this challenge. However, this novel approach of combining federated methods with multi-objective optimisation has never been discussed in the broader context of both fields. In this work, we offer a first clear and systematic overview of the different ways the two fields can be integrated. We propose a first taxonomy on the use of multi-objective methods in connection with Federated Learning, providing a targeted survey of the state-of-the-art and proposing unambiguous labels to categorise contributions. Given the developing nature of this field, our taxonomy is designed to provide a solid basis for further research, capturing existing works while anticipating future additions. Finally, we outline open challenges and possible directions for further research.
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