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A Weighted Average Consensus Approach for Decentralized Federated Learning

Machine Intelligence Research 2022年第4期19卷 319-330页

作者： Alessandro Giuseppi Sabato Manfredi antonio pietrabissa Department of Computer Controland Management EngineeringUniversity of Rome La SapienzaRome 00185Italy Department of Electrical Engineering and Information Technology University of Naples Federico ⅡNaples 80125Italy

Federated learning(FedL)is a machine learning(ML)technique utilized to train deep neural networks(DeepNNs)in a distributed way without the need to share data among the federated training *** was proposed for edge computing and Internet of things(IoT)tasks in which a centralized server was responsible for coordinating and governing the training *** remove the design limitation implied by the centralized entity,this work proposes two different solutions to decentralize existing FedL algorithms,enabling the application of FedL on networks with arbitrary communication topologies,and thus extending the domain of application of FedL to more complex scenarios and new *** the two proposed algorithms,one,called FedLCon,is developed based on results from discrete-time weighted average consensus theory and is able to reconstruct the performances of the standard centralized FedL solutions,as also shown by the reported validation tests.

关键词： Federated learning(FedL) deep learning federated averaging(FedAvg) machine learning(ML) artificial intelligence discrete-time consensus distributed systems.

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Robust and fault-tolerant spacecraft attitude control based on an extended-observer design

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Control Theory and Technology 2022年第3期20卷 323-337页

作者： Alessandro Giuseppi Francesco Delli Priscoli antonio pietrabissa Department of Computer Control and Management EngineeringLa SapienzaUniversity of RomeVia Ariosto 25Rome00185Italy

The aim of this work is to develop a robust control strategy able to drive the attitude of a spacecraft to a reference value,despite the presence of unknown but bounded uncertainties in the system parameters and external *** to the use of an extended observer design,the proposed control law is robust against all the uncertainties that affect the high-frequency gain matrix,which is shown to capture a broad spectrum of modelling issues,some of which are often neglected by traditional *** proposed controller then provides robustness against parametric uncertainties,as moment of inertia estimation,payload deformations,actuator faults and external disturbances,while maintaining its asymptotic properties.

关键词： Extended observer Spacecraft control Attitude stabilization

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Satellite Integration into 5G:Deep Reinforcement Learning for Network Selection

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Machine Intelligence Research 2022年第2期19卷 127-137页

作者： Emanuele De Santis Alessandro Giuseppi antonio pietrabissa Michael Capponi Francesco Delli Priscoli Department of Computer Control and Management Engineering“Antonio Ruberti”University of Rome La SapienzaRome 00185Italy

This paper proposes a deep-Q-network(DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov decision process(MDP) model of the problem. Network selection is an enabling technology for multi-connectivity, one of the core functionalities of 5G. For this reason, the present work considers a realistic network model that takes into account path-loss models and intra-RAT(radio access technology) interference. Numerical simulations validate the proposed approach and show the improvements achieved in terms of connection acceptance, resource allocation, and load *** particular, the DQN algorithm has been tested against classic reinforcement learning one and other baseline approaches.

关键词： Network selection HetNet deep reinforcement learning deep-Q-network(DQN) 5G communications

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