64 citations · 64 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Roadmap for Edge AI: A Dagstuhl Perspective
Aaron Yi Ding, Ella Peltonen, Tobias Meuser +16
Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred…
Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures
Hao Zhou, Atakan Aral, Ivona Brandic +1
Microgrids (MGs) are important players for the future transactive energy systems where a number of intelligent Internet of Things (IoT) devices interact for energy management in th…