A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward

【Author】 Majeed, Abdul; Hwang, Seong Oun

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】Federated learning (FL) is considered a de facto standard for privacy preservation in AI environments because it does not require data to be aggregated in some central place to train an AI model. Preserving data on the client side and sharing only the model's parameters with a central server preserves privacy while training an AI model of higher generalizability. Unfortunately, sharing the model's parameters with the server can create privacy leaks, and therefore, FL is unable to meet privacy requirements in many situations. Furthermore, FL is prone to other technical issues, such as data poisoning, model poisoning, fairness, client dropout, and convergence issues, to name just a few. In this work, we provide a multifaceted survey on FL, including its fundamentals, paradigm shifts, technical issues, recent developments, and future prospects. First, we discuss the fundamental concepts of FL (workflow, categorization, the differences between centralized learning and FL, and applications of FL in diverse fields), and we then discuss the paradigm shifts brought on by FL from a broader perspective (e.g., data use, AI model development, resource sharing, etc.). Later, we pinpoint ten practical issues currently hindering the viability of the FL landscape, and we discuss developments made under each issue by summarizing state-of-the-art (SOTA) literature. We highlight FL partnerships with two or more technologies that either improve practical aspects/issues in FL or extend its adoption to new areas/domains. We pinpoint various trade-offs that exist in an FL ecosystem, and the corresponding SOTA developments to mitigate them. We also discuss the latest studies that have been proposed to make FL trustworthy and beneficial for the community. Lastly, we suggest valuable research directions towards enhancing technical efficacy by guiding researchers to less explored topics in FL.

【Keywords】Servers; Surveys; Data models; Artificial intelligence; Data privacy; Training; Federated learning; Resource management; AI models; poisoning attacks; privacy preservation; training data

【发表时间】2024

【收录时间】2024-06-27

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