Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges
Yanli Li, Zhongliang Guo, Nan Yang, Huaming Chen, Dong Yuan, Weiping Ding

TL;DR
This survey comprehensively reviews threats to federated learning throughout its life cycle, analyzes defense strategies, and discusses challenges and future directions to enhance trustworthiness and privacy in FL systems.
Contribution
It provides a detailed classification of threats and defenses in federated learning, compares various strategies, and highlights research gaps and future challenges.
Findings
Identified key threats impacting utility and privacy in FL.
Analyzed and compared different defense frameworks and their trade-offs.
Outlined future research directions and existing bottlenecks.
Abstract
Federated Learning (FL) offers innovative solutions for privacy-preserving collaborative machine learning (ML). Despite its promising potential, FL is vulnerable to various attacks due to its distributed nature, affecting the entire life cycle of FL services. These threats can harm the model's utility or compromise participants' privacy, either directly or indirectly. In response, numerous defense frameworks have been proposed, demonstrating effectiveness in specific settings and scenarios. To provide a clear understanding of the current research landscape, this paper reviews the most representative and state-of-the-art threats and defense frameworks throughout the FL service life cycle. We start by identifying FL threats that harm utility and privacy, including those with potential or direct impacts. Then, we dive into the defense frameworks, analyze the relationship between threats…
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Taxonomy
TopicsInternet of Things and AI · Privacy-Preserving Technologies in Data · Cloud Data Security Solutions
Methodstravel james
