FedFm: Towards a Robust Federated Learning Approach For Fault Mitigation
at the Edge Nodes
- URL: http://arxiv.org/abs/2111.01074v1
- Date: Mon, 1 Nov 2021 16:35:00 GMT
- Title: FedFm: Towards a Robust Federated Learning Approach For Fault Mitigation
at the Edge Nodes
- Authors: Manupriya Gupta, Pavas Goyal, Rohit Verma, Rajeev Shorey, Huzur Saran
- Abstract summary: Federated Learning deviates from the norm of "send data to model" to "send model to data"
In this paper, we first analyse the impact of the number of edge devices on an FL model and provide a strategy to select an optimal number of devices that would contribute to the model.
We observe how the edge ecosystem behaves when the selected devices fail and provide a mitigation strategy to ensure a robust Federated Learning technique.
- Score: 1.9645939141861544
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated Learning deviates from the norm of "send data to model" to "send
model to data". When used in an edge ecosystem, numerous heterogeneous edge
devices collecting data through different means and connected through different
network channels get involved in the training process. Failure of edge devices
in such an ecosystem due to device fault or network issues is highly likely. In
this paper, we first analyse the impact of the number of edge devices on an FL
model and provide a strategy to select an optimal number of devices that would
contribute to the model. We observe how the edge ecosystem behaves when the
selected devices fail and provide a mitigation strategy to ensure a robust
Federated Learning technique.
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