2026 ASIAN Conference on Communication and Networks (ASIANComNet 2026)

Federated learning (FL) enables Industrial Internet of Things (IIoT) devices to collaboratively train models without exposing their raw data. However, synchronous FL is sensitive to heterogeneous and time-varying computation and communication conditions because each round is delayed by the slowest selected client. Existing latency-aware selection methods can mitigate straggler delays but may repeatedly favor resource-rich devices, thereby limiting the participation of slower clients. GATES is introduced as an adaptive groupwise client-selection framework that balances round-completion latency and long-term participation requirements. At each communication round, clients are sorted according to their estimated computation and upload latency and then partitioned into groups with similar latency profiles. Client-specific virtual queues track accumulated participation deficits, while a joint selection metric evaluates each group according to its completion latency and participation demand. The resulting design reduces the client-selection candidate space and limits latency dispersion within synchronous rounds. Experimental results demonstrate that GATES achieves accuracy comparable to FedAvg and FedProx while consistently reducing per-round training latency under representative non-IID conditions.

Federated Learning,Industrial Internet of Things (IIoT),Edge Intelligence,Resource Heterogeneity,Distributed Machine Learning,Client Selection