Federated learningNatural language processingPrivacySecuritySystematic literature reviewFederated learning (FL) is a decentralized machine learning (ML) framework that allows models to be trained without sharing the participants' local data. FL thus preserves privacy better than centralized machine learning. ...
This review paper thoroughly examines the necessity of forecasting models, methodologies, and data integrity, with a keen eye on the evolving landscape of Federated Learning (FL) in PV and WP forecasting. Commencing with an introduction highlighting the significance of forecasting models in optimising ...
the computational load is distributed across multiple clients, reducing the burden on central servers. This paper presents, to the best of the authors' knowledge, the first review discussing recent advancements of FL in CV applications, comparing them to conventional centralized training paradigms. It...
or Federated Deep Learning (FDL). Since it is impossible for me to know every single reference on FL, please pardon me if I missed any of your work. If you want me to add your work to this list, you are welcome to send me a message. I will try my best to keep...
Paper tables with annotated results for Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications
In this paper, we conduct a comprehensive survey on privacy and robustness in federated learning over the past 5 years. Through a concise introduction to the concept of FL, and a unique taxonomy covering: threat models; privacy attacks and defenses; ...
Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the raw
Literature review The concept of FL has significantly gained traction since its introduction by McMahan et al. in their pioneering 2017 paper9,“Communication-Efficient Learning of Deep Networks from Decentralized Data.” This foundational research highlighted FL as a pivotal response to the escalating...
Federated learning (FL) is a distributed machine learning paradigm allowing multiple clients to collaboratively train a global model without sharing their local data. However, FL entails exposing the model to various participants. This poses a risk of unauthorized model distribution or resale by the ...
Paper Fairness and Accuracy in Federated Learning In the federated learning setting, multiple clients jointly train a model under the coordination of the central server, while the training data is kept on the client to ensure privacy. Normally, inconsistent distribution of data across different ...