Aug 23, 2021 · To satisfy the need for flexible worker participation, we consider a new FL paradigm called "Anarchic Federated Learning" (AFL) in this paper.
We consider a new FL paradigm called Anarchic Federated. Learning (AFL), where the workers are allowed to engage in training at will and choose the number of ...
We propose a new FL paradigm called Anarchic Federated Learning (AFL), where the workers are allowed to engage in training at will and choose the number of ...
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Oct 16, 2023 · In this paper, we propose AFL-DGA, an Anarchic Federated Learning algorithm with Delayed Gradient Averaging, which gives maximum freedom to ...
Jan 28, 2022 · This paper proposes a general FL framework, called anarchic federated learning (AFL), that allows voluntary client participation of clients, ...
Proposed a new federated learning paradigm – Anarchic Federated. Learning (AFL). - From server-centric to worker-spontaneous. - Loose server-worker coupling.
In this paper, we propose AFLC, an Anarchic Federated Learning algorithm for Convex learning problems, which gives maximum freedom to clients.
We consider a new FL paradigm called Anarchic Federated Learning (AFL), where the workers are allowed to engage in training at will and choose the number of ...
In this paper, we propose AFLC, an Anarchic. Federated Learning algorithm for Convex learning problems, which gives maximum freedom to clients. In particular, ...
Federated learning (FL) is a fast-developing technique that allows multiple workers to train a global model based on a distributed dataset. Conventional FL ...