3 papers
cs.AR2026
A Fault-Tolerant Spike-Time Interface for Approximate Agreement in Distributed Neuromorphic Systems
Arman Ferdowsi, Maryam DehghanChenary, Kevin Tierney +1
Large neuromorphic systems contain many processing tiles that may replicate a shared control parameter such as a threshold reference. If these copies diverge, identical inputs may…
cs.NE2025
From Silicon to Spikes: System-Wide Efficiency Gains via Exact Event-Driven Training in Neuromorphic Computing
Arman Ferdowsi, Atakan Aral
Spiking neural networks (SNNs) promise orders-of-magnitude efficiency gains by communicating with sparse, event-driven spikes rather than dense numerical activations. However, most…
cs.DC2025
Clustered Federated Learning with Hierarchical Knowledge Distillation
Sabtain Ahmad, Meerzhan Kanatbekova, Ivona Brandic +1
Clustered Federated Learning (CFL) has emerged as a powerful approach for addressing data heterogeneity and ensuring privacy in large distributed IoT environments. By clustering cl…