Federated Learning (FL) has been an area of active research in recent years. There have been numerous studies in FL to make it more successful in the presence of data heterogeneity. However, despite the existence of many publications, the state of progress in the field is unknown. Many of the works use inconsistent experimental settings and there are no comprehensive studies on the effect of FL-specific experimental variables on the results and practical insights for a more comparable and consistent FL experimental setup. Furthermore, the existence of several benchmarks and confounding variables has further complicated the issue of inconsistency and ambiguity. In this work, we present the first comprehensive study on the effect of FL-specif...
Federated Learning is a novel framework that allows multiple devices or institutions to train a mach...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
As a distributed learning paradigm, Federated Learning (FL) faces the communication bottleneck issue...
Federated Learning (FL) has been an area of active research in recent years. There have been numerou...
Though successful, federated learning presents new challenges for machine learning, especially when ...
Federated learning (FL) has been proposed to protect data privacy and virtually assemble the isolate...
This paper presents the design and implementation of FLIPS, a middleware system to manage data and p...
We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scal...
The increasing size of data generated by smartphones and IoT devices motivated the development of Fe...
Federated learning allows the training of a model from the distributed data of many clients under th...
Federated learning (FL) is a decentralized machine learning (ML) method that enables model training ...
The advance of Machine Learning (ML) techniques has become the driving force in the development of A...
As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) ...
The uneven distribution of local data across different edge devices (clients) results in slow model ...
Federated learning is an upcoming machine learning concept which allows data from multiple sources b...
Federated Learning is a novel framework that allows multiple devices or institutions to train a mach...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
As a distributed learning paradigm, Federated Learning (FL) faces the communication bottleneck issue...
Federated Learning (FL) has been an area of active research in recent years. There have been numerou...
Though successful, federated learning presents new challenges for machine learning, especially when ...
Federated learning (FL) has been proposed to protect data privacy and virtually assemble the isolate...
This paper presents the design and implementation of FLIPS, a middleware system to manage data and p...
We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scal...
The increasing size of data generated by smartphones and IoT devices motivated the development of Fe...
Federated learning allows the training of a model from the distributed data of many clients under th...
Federated learning (FL) is a decentralized machine learning (ML) method that enables model training ...
The advance of Machine Learning (ML) techniques has become the driving force in the development of A...
As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) ...
The uneven distribution of local data across different edge devices (clients) results in slow model ...
Federated learning is an upcoming machine learning concept which allows data from multiple sources b...
Federated Learning is a novel framework that allows multiple devices or institutions to train a mach...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
As a distributed learning paradigm, Federated Learning (FL) faces the communication bottleneck issue...
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