A systematic review of federated learning: Challenges, aggregation methods, and development tools
BS Guendouzi, S Ouchani, HEL Assaad… - Journal of Network and …, 2023 - Elsevier
Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …
ized machine learning approach, facilitating collaborative model training across numerous …
Security and privacy threats to federated learning: Issues, methods, and challenges
Federated learning (FL) has nourished a promising method for data silos, which enables
multiple participants to construct a joint model collaboratively without centralizing data. The …
multiple participants to construct a joint model collaboratively without centralizing data. The …
Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges
Federated learning is a machine learning paradigm that emerges as a solution to the privacy-
preservation demands in artificial intelligence. As machine learning, federated learning is …
preservation demands in artificial intelligence. As machine learning, federated learning is …
Auditing privacy defenses in federated learning via generative gradient leakage
Federated Learning (FL) framework brings privacy benefits to distributed learning systems
by allowing multiple clients to participate in a learning task under the coordination of a …
by allowing multiple clients to participate in a learning task under the coordination of a …
Scalefl: Resource-adaptive federated learning with heterogeneous clients
Federated learning (FL) is an attractive distributed learning paradigm supporting real-time
continuous learning and client privacy by default. In most FL approaches, all edge clients …
continuous learning and client privacy by default. In most FL approaches, all edge clients …
A survey on gradient inversion: Attacks, defenses and future directions
Recent studies have shown that the training samples can be recovered from gradients,
which are called Gradient Inversion (GradInv) attacks. However, there remains a lack of …
which are called Gradient Inversion (GradInv) attacks. However, there remains a lack of …
A survey of what to share in federated learning: Perspectives on model utility, privacy leakage, and communication efficiency
Federated learning (FL) has emerged as a highly effective paradigm for privacy-preserving
collaborative training among different parties. Unlike traditional centralized learning, which …
collaborative training among different parties. Unlike traditional centralized learning, which …
[HTML][HTML] A review of medical federated learning: Applications in oncology and cancer research
A Chowdhury, H Kassem, N Padoy, R Umeton… - International MICCAI …, 2021 - Springer
Abstract Machine learning has revolutionized every facet of human life, while also becoming
more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare …
more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare …
Bayesian framework for gradient leakage
Federated learning is an established method for training machine learning models without
sharing training data. However, recent work has shown that it cannot guarantee data privacy …
sharing training data. However, recent work has shown that it cannot guarantee data privacy …
Trustworthy distributed ai systems: Robustness, privacy, and governance
Emerging Distributed AI systems are revolutionizing big data computing and data
processing capabilities with growing economic and societal impact. However, recent studies …
processing capabilities with growing economic and societal impact. However, recent studies …