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Extensive research is underway to meet the hyper-connectivity demands of 6Gnetworks, driven by applications like XR/VR and holographic communications,which generate substantial data requiring network-based processing,transmission, and analysis. However, adhering to diverse data privacy andsecurity policies in the anticipated multi-domain, multi-tenancy scenarios of6G presents a significant challenge. Federated Analytics (FA) emerges as apromising distributed computing paradigm, enabling collaborative data valuegeneration while preserving privacy and reducing communication overhead. FAapplies big data principles to manage and secure distributed heterogeneousnetworks, improving performance, reliability, visibility, and security withoutcompromising data confidentiality. This paper provides a comprehensive overviewof potential FA applications, domains, and types in 6G networks, elucidatinganalysis methods, techniques, and queries. It explores complementary approachesto enhance privacy and security in 6G networks alongside FA and discusses thechallenges and prerequisites for successful FA implementation. Additionally,distinctions between FA and Federated Learning are drawn, highlighting theirsynergistic potential through a network orchestration scenario.

Federated Analytics for 6G Networks: Applications, Challenges, and Opportunities