Data and Channel-Adaptive Sensor Scheduling for Federated Edge Learning via Over-the-Air Gradient Aggregation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2021.3096570
Abstract: Over-the-air gradient aggregation and data-aware scheduling have recently drawn great attention due to the outstanding performance in improving communication efficiency for federated edge learning applications. However, in this case, the estimated gradient suffers from the… read more here.
Keywords: federated edge; gradient aggregation; data channel; air gradient ... See more keywords
Online-Learning-Based Fast-Convergent and Energy-Efficient Device Selection in Federated Edge Learning
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3222234
Abstract: As edge computing faces increasingly severe data security and privacy issues of edge devices, a framework called federated edge learning (FEL) has recently been proposed to enable machine learning (ML) model training at the edge,… read more here.
Keywords: device selection; energy; edge; federated edge ... See more keywords
Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learning
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Journal on Selected Areas in Communications"
DOI: 10.1109/jsac.2021.3118348
Abstract: In this paper, we investigate over-the-air model aggregation in a federated edge learning (FEEL) system. We introduce a Markovian probability model to characterize the intrinsic temporal structure of the model aggregation series. With this temporal… read more here.
Keywords: aggregation; edge learning; temporal structure; model ... See more keywords
Dynamic Scheduling for Over-the-Air Federated Edge Learning With Energy Constraints
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal on Selected Areas in Communications"
DOI: 10.1109/jsac.2021.3126078
Abstract: Machine learning and wireless communication technologies are jointly facilitating an intelligent edge, where federated edge learning (FEEL) is emerging as a promising training framework. As wireless devices involved in FEEL are resource limited in terms… read more here.
Keywords: energy; air; federated edge; energy constraints ... See more keywords
Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal on Selected Areas in Communications"
DOI: 10.1109/jsac.2023.3242727
Abstract: To satisfy the expected plethora of computation-heavy applications, federated edge learning (FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency and privacy-preserving. To further improve the efficiency of wireless data… read more here.
Keywords: computation; federated edge; air computation; edge learning ... See more keywords
Privacy-Preserving Federated Edge Learning: Modeling and Optimization
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2022.3167088
Abstract: In this letter, we consider the personalized differential privacy (DP) based federated edge learning system. Each edge device adds DP noise to its local machine learning (ML) model updates to prevent the private information contained… read more here.
Keywords: federated edge; loss; privacy; edge learning ... See more keywords
Friend-as-Learner: Socially-Driven Trustworthy and Efficient Wireless Federated Edge Learning
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Mobile Computing"
DOI: 10.1109/tmc.2021.3074816
Abstract: Recently, wireless edge networks have realized intelligent operation and management with edge artificial intelligence (AI) techniques (i.e., federated edge learning). However, the trustworthiness and effective incentive mechanisms of federated edge learning (FEL) have not been… read more here.
Keywords: wireless; trustworthy efficient; federated edge; edge learning ... See more keywords
Federated Edge Network Utility Maximization for a Multi-Server System: Algorithm and Convergence
Sign Up to like & getrecommendations! Published in 2022 at "IEEE/ACM Transactions on Networking"
DOI: 10.1109/tnet.2022.3156530
Abstract: We propose a novel Federated Edge Network Utility Maximization (FEdg-NUM) architecture for solving a large-scale distributed network utility maximization (NUM) problem. In FEdg-NUM, clients with private utilities communicate to a peer-to-peer network of edge servers.… read more here.
Keywords: network utility; network; utility maximization; federated edge ... See more keywords
DEEP-FEL: Decentralized, Efficient and Privacy-Enhanced Federated Edge Learning for Healthcare Cyber Physical Systems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Network Science and Engineering"
DOI: 10.1109/tnse.2022.3175945
Abstract: The rapid development of Internet of Things (IoT) stimulates the innovation for the health-related devices such as remote patient monitoring, connected inhalers and ingestible sensors. Simultaneously, with the aid of numerous equipments, a great number… read more here.
Keywords: enhanced federated; federated edge; efficient privacy; privacy enhanced ... See more keywords
One Bit Aggregation for Federated Edge Learning With Reconfigurable Intelligent Surface: Analysis and Optimization
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Wireless Communications"
DOI: 10.1109/twc.2022.3198881
Abstract: As one of the most popular and attractive frameworks for model training, federated edge learning (FEEL) presents a new paradigm, which avoids direct data transmission by collaboratively training a global learning model across multiple distributed… read more here.
Keywords: bit; federated edge; one bit; edge learning ... See more keywords
High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks
Sign Up to like & getrecommendations! Published in 2022 at "China Communications"
DOI: 10.23919/jcc.2023.03.001
Abstract: Federated edge learning (FEEL) technology for vehicular networks is considered as a promising technology to reduce the computation workload while keeping the privacy of users. In the FEEL system, vehicles upload data to the edge… read more here.
Keywords: federated edge; vehicular networks; scheme; edge learning ... See more keywords