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   Articles with "federated edge" as a keyword



Data and Channel-Adaptive Sensor Scheduling for Federated Edge Learning via Over-the-Air Gradient Aggregation

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recommendations! 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

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recommendations! 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

Dynamic Resource Management for Federated Edge Learning With Imperfect CSI: A Deep Reinforcement Learning Approach

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recommendations! Published in 2024 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2024.3414472

Abstract: Federated edge learning (FEL) has become a research hotspot to relieve the computational burden on servers and protect users’ data privacy. In an FEL system, adjusting the client selection and resource allocation scheme can reduce… read more here.

Keywords: dynamic resource; federated edge; deep reinforcement; edge learning ... See more keywords

Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning

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recommendations! Published in 2025 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2025.3590056

Abstract: Federated learning (FL) provides a privacy-preserving framework for training machine learning models on mobile-edge devices. Traditional FL algorithms, e.g., FedAvg, impose a heavy communication workload on these devices. To mitigate this issue, hierarchical federated edge… read more here.

Keywords: resource; edge; federated edge; topology ... See more keywords

Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learning

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recommendations! 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

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recommendations! 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

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recommendations! 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

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recommendations! 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

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recommendations! 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

Debiased Device Sampling for Federated Edge Learning in Wireless Networks

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recommendations! Published in 2025 at "IEEE Transactions on Mobile Computing"

DOI: 10.1109/tmc.2024.3464740

Abstract: As a privacy-preserved distributed machine learning paradigm, federated edge learning (FEL) was designed to absorb knowledge from user devices to construct intelligent services without transmitting raw data. However, this paradigm depends on the local training… read more here.

Keywords: device; federated edge; wireless networks; device sampling ... See more keywords

Heterogeneity-Aware Cooperative Federated Edge Learning With Adaptive Computation and Communication Compression

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recommendations! Published in 2024 at "IEEE Transactions on Mobile Computing"

DOI: 10.1109/tmc.2024.3492916

Abstract: Motivated by the drawbacks of cloud-based federated learning (FL), cooperative federated edge learning (CFEL) has been proposed to improve efficiency for FL over mobile edge networks, where multiple edge servers collaboratively coordinate the distributed model… read more here.

Keywords: cooperative federated; edge; heterogeneity aware; federated edge ... See more keywords