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Exploiting Over-The-Air Consensus for Collision Avoidance and Formation Control in Multi-Agent Systems
This paper introduces a distributed control method for multi-agent robotic systems employing Over the Air Consensus (OTA-Consensus).
Boosting Fairness and Robustness in Over-the-Air Federated Learning
Over-the-Air Computation is a beyond-5G communication strategy that has recently been shown to be useful for the decentralized training of machine learning models due to its efficiency.
Federated Learning in Wireless Networks via Over-the-Air Computations
This strategy, often called federated learning, is mainly employed for two reasons: (i) improving resource-efficiency by avoiding to share potentially large datasets and (ii) guaranteeing privacy of local agents' data.
Collective Iterative Learning Control: Exploiting Diversity in Multi-Agent Systems for Reference Tracking Tasks
We show that the proposed method allows the collective to combine the advantages of the agents' individual learning strategies and thereby overcomes trade-offs and limitations of single-agent ILC.
Low Complexity Method for Simulation of Epidemics Based on Dijkstra's Algorithm
Models of epidemics over networks have become popular, as they describe the impact of individual behavior on infection spread.
