CoomFL: Improving Global and Local Model Coordination Strategies for Heterogeneous Medical Image Tasks in Federated Learning
YH Mu, P Liu - 2023 Asia Symposium on Image Processing …, 2023 - ieeexplore.ieee.org
… of client drift are crucial for federated learning systems. … model has good adaptability on
different clients, we consider … , we design a dual momentum update pattern and apply the …
different clients, we consider … , we design a dual momentum update pattern and apply the …
Accelerating Communication-efficient Federated Multi-Task Learning With Personalization and Fairness
… efficient federated learning approach is proposed in this paper where the momentum technique
… selection method is proposed by FedMoS [37] to accelerate the convergence rate, where …
… selection method is proposed by FedMoS [37] to accelerate the convergence rate, where …
SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning
… FedMoS keeps customized momentum buffers on both server … the adaptability of our approach
to different client numbers and … FedMoS: Taming Client Drift in Federated Learning with …
to different client numbers and … FedMoS: Taming Client Drift in Federated Learning with …
Efficient and straggler-resistant homomorphic encryption for heterogeneous federated learning
… Abstract—Cross-silo federated learning (FL) enables multiple institutions (clients) to …
Fedmos: Taming client drift in federated learning with double momentum and adaptive selection…
Fedmos: Taming client drift in federated learning with double momentum and adaptive selection…
DFLStar: A Decentralized Federated Learning Framework with Self-Knowledge Distillation and Participant Selection
… To tackle the client drift challenge caused by non-IID data, we propose self-knowledge
distillation … GossipFL: A decentralized federated learning framework with sparsified and adaptive …
distillation … GossipFL: A decentralized federated learning framework with sparsified and adaptive …
Multi-level Personalised Federated Learning: A concept for data sovereign machine learning in digital agriculture
M Hussaini, A Stein - INFORMATIK 2024, 2024 - dl.gi.de
… Federated Learning is a ML paradigm that … , client-drift can occur and needs to be addressed.
In this concept paper, we propose the advancement of Personalised Federated Learning …
In this concept paper, we propose the advancement of Personalised Federated Learning …
Fast-Convergent Wireless Federated Learning: A Voting Based TopK Model Compression Approach
… , called FedMoS [19], with an adaptive client selection scheme … , federated learning (FL)
gains momentum because it is prospective in preserving data privacy during machine learning …
gains momentum because it is prospective in preserving data privacy during machine learning …