A D2D-Aided Federated Learning Scheme With Incentive Mechanism in 6G Networks
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2022.3232440
Abstract: Pervasive new era applications are expected to involve massive amount of data to implement intelligent distributed frameworks based on machine learning, supported by sixth generation (6G) networks technology to offer fast and reliable communications. Federated… read more here.
Keywords: incentive mechanism; federated learning; d2d aided; mechanism ... See more keywords
Data Distribution-Aware Online Client Selection Algorithm for Federated Learning in Heterogeneous Networks
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Vehicular Technology"
DOI: 10.1109/tvt.2022.3205307
Abstract: Federated learning (FL) has received significant attention as a practical alternative to traditional cloud-centric machine learning (ML). The performance (e.g., accuracy and convergence time) of FL is hampered by the selection of clients having non-independent… read more here.
Keywords: client selection; selection; convergence time;
Convergence Time Optimization for Federated Learning Over Wireless Networks
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Wireless Communications"
DOI: 10.1109/twc.2020.3042530
Abstract: In this paper, the convergence time of federated learning (FL), when deployed over a realistic wireless network, is studied. In particular, a wireless network is considered in which wireless users transmit their local FL models… read more here.
Keywords: convergence; convergence time; model; wireless ... See more keywords
Organizational Structure, Subsystem Interaction Pattern, and Misalignments in Complex NPD Projects
Sign Up to like & getrecommendations! Published in 2019 at "Production and Operations Management"
DOI: 10.1111/poms.13102
Abstract: Developing a complex new product requires the firm both to deconstruct that product into subsystems and to create an organizational structure aligned with the product architecture. However, empirical evidence indicates that misalignments do occur and… read more here.
Keywords: organizational structure; interaction pattern; convergence time; spurious communications ... See more keywords
Optimizing the learning rate for adaptive estimation of neural encoding models
Sign Up to like & getrecommendations! Published in 2018 at "PLoS Computational Biology"
DOI: 10.1371/journal.pcbi.1006168
Abstract: Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are… read more here.
Keywords: state; error; convergence time; rate ... See more keywords