… departure from standard approaches for large-scale machine learning, distributed optimi…
and challenges of federated learning, provide a broad overview of current approaches, and …

… interfaces make users can apply the included machine learning method to process federated
training. FC API, the basic layer for federation learning, serving for distributed computation. …

… federated learning in distributed environment have been analysed. The Federated-learning
framework model is implemented in centralized, decentralized and heterogeneous approach…

… We propose a novel federated learning system which provides formal privacy guarantees, …
compared with existing privacy-preserving approaches. Data never leaves the participants …

… In Federated Learning, we aim to train models across … a personalized variant of the federated
learning in which our goal is … This approach keeps all the benefits of the federated learning …

… research directions of federated learning. Finally, we summarize the characteristics of
existing federated learning, and analyze the current practical application of federated learning. …

… the FL domain, this report discusses the opportunities and challenges in federated learning.
… , Federated Learning can be better option.Federated Learning is a collaborative learning …

… But this approach differs significantly from the previous work on federated learning. … To
address this statistical challenge of federated learning, we show in Section 3 that the accuracy …

… features of federated ML, which differentiate it from other decentralized learning approaches.
Building on this, we discuss several key applications of the federated learning framework in …

… To address the data privacy leakage issue, we incorporate a privacy-preserving ML
technique named federated learning (FL) [10] for TFP in this article. In FL, distributed organizations …