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With the rapid advancement of 5G networks, billions of smart Internet ofThings (IoT) devices along with an enormous amount of data are generated at thenetwork edge. While still at an early age, it is expected that the evolving 6Gnetwork will adopt advanced artificial intelligence (AI) technologies tocollect, transmit, and learn this valuable data for innovative applications andintelligent services. However, traditional machine learning (ML) approachesrequire centralizing the training data in the data center or cloud, raisingserious user-privacy concerns. Federated learning, as an emerging distributedAI paradigm with privacy-preserving nature, is anticipated to be a key enablerfor achieving ubiquitous AI in 6G networks. However, there are several systemand statistical heterogeneity challenges for effective and efficient FLimplementation in 6G networks. In this article, we investigate the optimizationapproaches that can effectively address the challenging heterogeneity issuesfrom three aspects: incentive mechanism design, network resource management,and personalized model optimization. We also present some open problems andpromising directions for future research.

Optimization Design for Federated Learning in Heterogeneous 6G Networks