Federated learning (FL) is a distributed learning paradigm that enables a large number of
devices to collaboratively learn a model without sharing their raw data. Despite its practical …

… We aim to generalize our system from Federated Learning to Federated Computation,
which follows the same basic principles as described in this paper, but does not restrict …

… determined by learning accuracy level, and thus (ii) between the Federated Learning
time and UE energy consumption. We fill this gap by formulating a Federated Learning over …

… This is the concept behind federated learning (FL), in which the communication process is
carefully designed such that the data of an individual organization or device remain private. …

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

… design the incentive mechanism, the federated learning system could obtain a very good
machine learning … on the incentive mechanism design for federated learning, yet they do not …

… federated learning from the machine learning technique aspects, the software architecture
design concerns in building federated learning … the design challenges 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 …

… 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. …

… 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 …