FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
Federated Learning (FL) enables distributed Artificial Intelligence (AI) across cloud-edge environments by allowing collaborative model training without centralizing data. In cross-device deployments, FL systems face strict communication and participation constraints, as well as strong non-independent and identically distributed (non-IID) data that degrades convergence and model quality. Since only a subset of devices (a.k.a clients) can participate per training round, intelligent client selection becomes a key systems challenge. This paper proposes FedLECC (Federated Learning with Enhanced Cluster Choice), a lightweight, cluster-aware, and loss-guided client selection strategy for cross-...
| Authors | Daniel M. Jimenez-Gutierrez, Giovanni Giunta, Mehrdad Hassanzadeh et al. |
|---|---|
| Primary category | cs.DC |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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