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A review on client selection models in federated learning
Federated learning (FL) is a decentralized machine learning (ML) technique that enables
multiple clients to collaboratively train a common ML model without them having to share …
multiple clients to collaboratively train a common ML model without them having to share …
Cooperative multi-agent reinforcement learning: asynchronous communication and linear function approximation
We study multi-agent reinforcement learning in the setting of episodic Markov decision
processes, where many agents cooperate via communication through a central server. We …
processes, where many agents cooperate via communication through a central server. We …
SPACE: single-round participant amalgamation for contribution evaluation in federated learning
The evaluation of participant contribution in federated learning (FL) has recently gained
significant attention due to its applicability in various domains, such as incentive …
significant attention due to its applicability in various domains, such as incentive …
FedCust: Offloading hyperparameter customization for federated learning
Federated Learning (FL) is a new machine learning paradigm that enables training models
collaboratively across clients without sharing private data. In FL, data is non-uniformly …
collaboratively across clients without sharing private data. In FL, data is non-uniformly …
RCSR: Robust Client Selection and Replacement in Federated Learning
In Federated Learning (FL), to improve the training efficiency, we don't need to let all of the
clients join in the training process. Instead, we can select some specific clients to join in the …
clients join in the training process. Instead, we can select some specific clients to join in the …
AMFL: Asynchronous Multi-level Federated Learning with Client Selection
Synchronous Federated Learning (FL) may suffer from increased training time and costs. To
address this issue, Asynchronous Federated Learning (AFL) has been proposed …
address this issue, Asynchronous Federated Learning (AFL) has been proposed …
Decentralized Condition Monitoring for Distributed Wind Systems: A Federated Learning-Based Approach to Enhance SCADA Data Privacy
The paper presents a new condition monitoring method for distributed wind systems (DWSs)
by combining federated learning with supervisory control and data acquisition (SCADA) …
by combining federated learning with supervisory control and data acquisition (SCADA) …