Feb 14, 2022 · Firstly, we use a Multi-Armed Bandit (MAB) approach to identify the heterogenous communication and computation capabilities of clients, based on ...
Firstly, we use a Multi-Armed Bandit (MAB) approach to identify the heterogenous communication and computation ca- pabilities of clients, based on which, we ...
Firstly, we use a Multi-Armed Bandit (MAB) approach to identify the heterogenous communication and computation capabilities of clients, based on which, we ...
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What is heterogeneous federated learning?
What is asynchronous federated learning?
[PDF] Async-HFL: Efficient and Robust Asynchronous Federated Learning ...
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Heterogeneous Semi-Asynchronous Federated Learning in Internet of Things: A Multi-Armed Bandit Approach. IEEE Transactions on Emerging. Topics in ...
In this paper, we propose an asynchronous and hierarchical framework (Async-HFL) for performing FL in a common three-tier IoT network architecture.
Heterogeneous Semi-Asynchronous Federated Learning in Internet of Things: A Multi-Armed Bandit Approach. IEEE Transactions on Emerging. Topics in ...
Though asynchronous FL can well tackle the edge heterogeneity, it requires frequent model transfers, resulting in massive communication resource consumption.
Aug 22, 2023 · Federated learning (FL) is a kind of distributed machine learning framework, where the global model is generated on the centralized aggregation ...
Missing: Armed Bandit
Oct 11, 2023 · This paper introduces a blockchain-based asynchronous federated learning protection framework (BCAFL). It introduces model validation and incentive mechanisms.
This paper proposes an efficient FL algorithm, named CSFedAvg, in which the clients with lower degree of non-IID data will be chosen to train the models ...