Multi-objective methods in Federated Learning: A survey and taxonomy

• URL: http://arxiv.org/abs/2502.03108v1
• Date: Wed, 05 Feb 2025 12:06:43 GMT
• Title: Multi-objective methods in Federated Learning: A survey and taxonomy
• Authors: Maria Hartmann, Grégoire Danoy, Pascal Bouvry,
• Abstract summary: We propose a first taxonomy on the use of multi-objective methods in connection with Federated Learning. We outline open challenges and possible directions for further research.
• Score: 2.519319150166215
• License:
• Abstract: 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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