Titlebook: Handbook of Trustworthy Federated Learning; My T. Thai,Hai N. Phan,Bhavani Thuraisingham Book 2025 The Editor(s) (if applicable) and The A

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Trustworthiness, Privacy, and Security in Federated Learninghe regulatory requirements of laws and regulations are becoming increasingly strict, bringing huge impacts and challenges to enterprises with user’s personal data such as Internet services and financial technology. Up to a point, federal learning ensures data privacy by storing and processing person

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Secure Federated Learningt sharing their raw data. It addresses the challenge of data privacy in distributed environments by allowing data to remain decentralized while still benefiting from the collective knowledge. However, due to this collaborative training of a shared model, it has been known that FL is susceptible to v

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Needlework

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Unfair Trojan: Targeted Backdoor Attacks Against Model Fairness they are becoming increasingly vulnerable to various forms of attacks, such as backdoor and data poisoning attacks that can have adverse effects on model behavior. These attacks become more prevalent and complex in federated learning, where multiple local models contribute to a single global model

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Federated Bilevel Optimizationsearch, meta learning, etc. To facilitate those machine learning models to federated learning, federated bilevel optimization has been actively studied recently. To deepen the understanding of federated bilevel optimization and advance its development, this chapter discusses the unique challenges, s

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Privacy in Federated Learning Natural Language Models individual training examples, which severely affects the privacy and security of private datasets. In this chapter, we will discuss training language models in Federated Learning and its privacy and security challenges of the training process. We introduce a novel concept of user-entity differentia

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Federated Learning of Models Pretrained on Different Features with Consensus Graphsce. Existing federated learning paradigms enable this via model aggregation that enforces a strong form of modeling homogeneity and synchronicity across clients. This is however not suitable to many practical scenarios. For example, in distributed sensing, heterogeneous sensors reading data from dif