Introduces M-DSL algorithm for distributed swarm learning that selects workers using a new non-i.i.d. degree metric to improve convergence and accuracy under data heterogeneity, with theoretical analysis and experiments on heterogeneous datasets.
Enhancing reliability of distributed learning over edge networks,
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Multi-Worker Selection based Distributed Swarm Learning for Edge IoT with Non-i.i.d. Data
Introduces M-DSL algorithm for distributed swarm learning that selects workers using a new non-i.i.d. degree metric to improve convergence and accuracy under data heterogeneity, with theoretical analysis and experiments on heterogeneous datasets.