DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset
Federated Learning (FL) is a distributed learning paradigm that can coordinate
heterogeneous edge devices to perform model training without sharing private data. While …
heterogeneous edge devices to perform model training without sharing private data. While …
Demystifying impact of key hyper-parameters in federated learning: A case study on CIFAR-10 and FashionMNIST
Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving
distributed Machine Learning (ML), enabling model training across distributed devices …
distributed Machine Learning (ML), enabling model training across distributed devices …
Communication-Efficient Federated Learning with Adaptive Aggregation for Heterogeneous Client-Edge-Cloud Network
Client-edge-cloud Federated Learning (CEC-FL) is emerging as an increasingly popular FL
paradigm, alleviating the performance limitations of conventional cloud-centric Federated …
paradigm, alleviating the performance limitations of conventional cloud-centric Federated …
Joint Data Sampling and Client Scheduling for Over-the-Air Federated Learning
Wireless federated learning (FL) can effectively exploit the rich data distributed at edge
devices for network edge intelligence. However, the contradictions between the huge …
devices for network edge intelligence. However, the contradictions between the huge …