Add to ORKGThis paper presents the design and implementation of FLIPS, a middleware system to manage data and participant heterogeneity in federated learning (FL) training workloads. In particular, we examine the benefits of label distribution clustering on participant selection in federated learning. FLIPS clusters parties involved in an FL training job based on the label distribution of their data apriori, and during FL training, ensures that each cluster is equitably represented in the participants selected. FLIPS can support the most common FL algorithms, including FedAvg, FedProx, FedDyn, FedOpt and FedYogi. To manage platform heterogeneity and dynamic resource availability, FLIPS incorporates a straggler management mechanism to handle changing c...
Federated learning (FL) enables collaborative learning between parties, called clients, without shar...
Though successful, federated learning presents new challenges for machine learning, especially when ...
Federated learning (FL) has emerged as a new paradigm for privacy-preserving computation in recent y...
Personalized decision-making can be implemented in a Federated learning (FL) framework that can coll...
As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) ...
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing...
Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine le...
Training ML models which are fair across different demographic groups is of critical importance due ...
Federated Learning (FL) has been an area of active research in recent years. There have been numerou...
Federated Learning (FL) has been an area of active research in recent years. There have been numerou...
Federated Learning (FL) is one of the leading learning paradigms for enabling a more significant pre...
The uneven distribution of local data across different edge devices (clients) results in slow model ...
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among di...
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...
Federated learning (FL) enables collaborative learning between parties, called clients, without shar...
Though successful, federated learning presents new challenges for machine learning, especially when ...
Federated learning (FL) has emerged as a new paradigm for privacy-preserving computation in recent y...
Personalized decision-making can be implemented in a Federated learning (FL) framework that can coll...
As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) ...
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing...
Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine le...
Training ML models which are fair across different demographic groups is of critical importance due ...
Federated Learning (FL) has been an area of active research in recent years. There have been numerou...
Federated Learning (FL) has been an area of active research in recent years. There have been numerou...
Federated Learning (FL) is one of the leading learning paradigms for enabling a more significant pre...
The uneven distribution of local data across different edge devices (clients) results in slow model ...
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among di...
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...
Federated learning (FL) enables collaborative learning between parties, called clients, without shar...
Though successful, federated learning presents new challenges for machine learning, especially when ...
Federated learning (FL) has emerged as a new paradigm for privacy-preserving computation in recent y...