Abstract In Federated Learning, we aim to train models across multiple computing units
(users), while users can only communicate with a common central server, without …

In Federated Learning, we aim to train models across multiple computing units (users), while
users can only communicate with a common central server, without exchanging their data …

Federated learning allows for the training of a model using data on multiple clients without
the clients transmitting that raw data. However the standard method is to transmit model …

Federated Learning (FL) refers to learning a high quality global model based on
decentralized data storage, without ever copying the raw data. A natural scenario arises with …

Federated learning (FL) research has made progress in developing algorithms for
distributed learning of global models, as well as algorithms for local personalization of those …

A key learning scenario in large-scale applications is that of federated learning, where a
centralized model is trained based on data originating from a large number of clients. We …

While federated learning traditionally aims to train a single global model across
decentralized local datasets, one model may not always be ideal for all participating clients …

Investigation of the degree of personalization in federated learning algorithms has shown
that only maximizing the performance of the global model will confine the capacity of the …

Existing federated learning paradigms usually extensively exchange distributed models at a
central solver to achieve a more powerful model. However, this would incur severe …

Deep neural networks have shown the ability to extract universal feature representations
from data such as images and text that have been useful for a variety of learning tasks …