Efficient Scheduling for Multi-Job Federated Learning Systems with Client Sharing
Federated Learning (FL) has emerged as a promising learning approch for data distributed
across edge devices. Existing research mainly focuses on single-job FL systems. However …
across edge devices. Existing research mainly focuses on single-job FL systems. However …
Heterogeneous Federated Learning for Balancing Job Completion Time and Model Accuracy
Federated Learning (FL) is a secure distributed learning paradigm, which enables
potentially a large number of devices to collaboratively train a global model based on their …
potentially a large number of devices to collaboratively train a global model based on their …
THF: 3-way hierarchical framework for efficient client selection and resource management in federated learning
M Asad, A Moustafa, FA Rabhi… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
Federated learning (FL) is a promising technique for collaboratively training machine-
learning models on massively distributed clients data under privacy constraints. However …
learning models on massively distributed clients data under privacy constraints. However …
Multi-job intelligent scheduling with cross-device federated learning
Recent years have witnessed a large amount of decentralized data in various (edge)
devices of end-users, while the decentralized data aggregation remains complicated for …
devices of end-users, while the decentralized data aggregation remains complicated for …
Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments
Federated Learning (FL) is an emerging machine learning paradigm that enables the
collaborative training of a shared global model across distributed clients while keeping the …
collaborative training of a shared global model across distributed clients while keeping the …
Asynchronous hierarchical federated learning based on bandwidth allocation and client scheduling
J Yang, Y Zhou, W Wen, J Zhou, Q Zhang - Applied Sciences, 2023 - mdpi.com
Federated learning (FL) offers a promising solution in edge computing to overcome
bandwidth limitations and privacy concerns associated with traditional cloud-based training …
bandwidth limitations and privacy concerns associated with traditional cloud-based training …
Balancing Federated Learning Trade-Offs for Heterogeneous Environments
Federated Learning (FL) is an enabling technology for supporting distributed machine
learning across several de-vices on decentralized data. A critical challenge when FL in …
learning across several de-vices on decentralized data. A critical challenge when FL in …
A comprehensive empirical study of heterogeneity in federated learning
AM Abdelmoniem, CY Ho… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
Federated learning (FL) is becoming a popular paradigm for collaborative learning over
distributed, private data sets owned by nontrusting entities. FL has seen successful …
distributed, private data sets owned by nontrusting entities. FL has seen successful …
AUCTION: Automated and quality-aware client selection framework for efficient federated learning
The emergency of federated learning (FL) enables distributed data owners to collaboratively
build a global model without sharing their raw data, which creates a new business chance …
build a global model without sharing their raw data, which creates a new business chance …
Fairness-Aware Job Scheduling for Multi-Job Federated Learning
Federated learning (FL) enables multiple data owners (aka FL clients) to collaboratively train
machine learning models without disclosing sensitive private data. Existing FL research …
machine learning models without disclosing sensitive private data. Existing FL research …