Trustworthy Federated Learning: Privacy, Security, and Beyond

2024-11-03 · Chunlu Chen, Ji Liu, Haowen Tan, Xingjian Li, Kevin I-Kai Wang, Peng Li, Kouichi Sakurai, Dejing Dou

While recent years have witnessed the advancement in big data and Artificial Intelligence (AI), it is of much importance to safeguard data privacy and security. As an innovative approach, Federated Learning (FL) addresses these concerns by facilitating collaborative model training across distributed data sources without transferring raw data. However, the challenges of robust security and privacy across decentralized networks catch significant attention in dealing with the distributed data in FL. In this paper, we conduct an extensive survey of the security and privacy issues prevalent in FL, underscoring the vulnerability of communication links and the potential for cyber threats. We delve into various defensive strategies to mitigate these risks, explore the applications of FL across different sectors, and propose research directions. We identify the intricate security challenges that arise within the FL frameworks, aiming to contribute to the development of secure and efficient FL systems.

📄 PDF Abstract BibTeX arXiv:2411.01583

Code (0)

등록된 구현이 없습니다.

Tasks

Federated Learning

Methods 이 논문이 사용한 방법론

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Attention 설명 없음

A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy

2023-02-21 · Yifei Zhang, Dun Zeng, Jinglong Luo, Zenglin Xu 외

Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society. Among various AI technologies, Federated Learning (FL) stands out as a promising solution for diverse…

Drug Discovery Federated Learning

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems

2026-06-24 · Balamurugan Palanisamy, G S S Chalapathi, Vikas Hassija, Rajkumar Buyya arxiv

Retrieval-Augmented Generation (RAG) has emerged as a dominant paradigm for enhancing large language models with external knowledge. By coupling retrieval mechanisms with generative models, RAG systems improve factual gr…

Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach

2024-05-27 · Xuhan Zuo, Minghao Wang, Tianqing Zhu, Lefeng Zhang 외

With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative. Traditional unlearning methods,…

Federated Learning Machine Unlearning Management

SafeLM: Unified Privacy-Aware Optimization for Trustworthy Federated Large Language Models

2026-04-17 · Noor Islam S. Mohammad, Uluğ Bayazıt arxiv

Large language models (LLMs) are increasingly deployed in high-stakes domains, yet a unified treatment of their overlapping safety challenges remains lacking. We present SafeLM, a framework that jointly addresses four pi…

Adversarial Robustness

FederatedTrust: A Solution for Trustworthy Federated Learning

2023-02-20 · Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Ning Xie, Gérôme Bovet 외

The rapid expansion of the Internet of Things (IoT) and Edge Computing has presented challenges for centralized Machine and Deep Learning (ML/DL) methods due to the presence of distributed data silos that hold sensitive …

Edge-computing Fairness Federated Learning Privacy Preserving