Discomfort, Doubt, and the Edge of Learning
Sign Up to like & getrecommendations! Published in 2022 at "Academic Medicine"
DOI: 10.1097/acm.0000000000004588
Abstract: Discomfort is a constant presence in the practice of medicine and an oft-ignored feature of medical education. Nonetheless, if approached with thoughtfulness, patience, and understanding, discomfort may play a critical role in the education of… read more here.
Keywords: medicine; doubt edge; discomfort doubt; edge learning ... See more keywords
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
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
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
Lessons Learned From Accident of Autonomous Vehicle Testing: An Edge Learning-Aided Offloading Framework
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2020.2984620
Abstract: This letter proposes an edge learning-based offloading framework for autonomous driving, where the deep learning tasks can be offloaded to the edge server to improve the inference accuracy while meeting the latency constraint. Since the… read more here.
Keywords: framework; inference accuracy; offloading framework; edge learning ... See more keywords
Edge Learning with Timeliness Constraints: Challenges and Solutions
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Communications Magazine"
DOI: 10.1109/mcom.001.2000382
Abstract: Future machine learning (ML) powered applications, such as autonomous driving and augmented reality, involve training and inference tasks with timeliness requirements and are communication- and computation-intensive, which demands the edge learning framework. The real-time requirements… read more here.
Keywords: inference; timeliness constraints; edge learning; delay ... See more keywords
Edge-Learning-Based Hierarchical Prefetching for Collaborative Information Streaming in Social IoT Systems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Computational Social Systems"
DOI: 10.1109/tcss.2020.3041171
Abstract: For smart cities, ubiquitous user connectivity and collaborative computation offloading are significant for the ever-increasing information requirements to promote the quality of citizens’ life. In this article, we design an information prefetching architecture, which investigates… read more here.
Keywords: information; edge learning; information streaming; learning based ... 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
Decentralized Edge Learning via Unreliable Device-to-Device Communications
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Wireless Communications"
DOI: 10.1109/twc.2022.3172147
Abstract: Distributed machine learning has been extensively employed in wireless systems, which can leverage abundant data distributed over massive devices to collaboratively train a high-quality global model. The research efforts of recent works have focused on… read more here.
Keywords: decentralized edge; unreliable device; edge learning; via unreliable ... 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