References of "Duong, Trung Q."
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See detailFedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud System
Nguyen, van Dinh UL; Chatzinotas, Symeon UL; Ottersten, Björn UL et al

in IEEE Transactions on Wireless Communications (in press)

Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. To address key challenges of ... [more ▼]

Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. To address key challenges of enabling FL over a wireless fogcloud system (e.g., non-i.i.d. data, users’ heterogeneity), we first propose an efficient FL algorithm based on Federated Averaging (called FedFog) to perform the local aggregation of gradient parameters at fog servers and global training update at the cloud. Next, we employ FedFog in wireless fog-cloud systems by investigating a novel network-aware FL optimization problem that strikes the balance between the global loss and completion time. An iterative algorithm is then developed to obtain a precise measurement of the system performance, which helps design an efficient stopping criteria to output an appropriate number of global rounds. To mitigate the straggler effect, we propose a flexible user aggregation strategy that trains fast users first to obtain a certain level of accuracy before allowing slow users to join the global training updates. Extensive numerical results using several real-world FL tasks are provided to verify the theoretical convergence of FedFog. We also show that the proposed co-design of FL and communication is essential to substantially improve resource utilization while achieving comparable accuracy of the learning model. [less ▲]

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See detailEmerging Techniques and Applications for 5G Networks and Beyond
Nguyen, van Dinh UL; Duong, Trung Q; Vien, Quoc-Tuan

in Mobile Networks and Applications (2020)

Detailed reference viewed: 72 (6 UL)
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See detailEnergy Minimization for Cache-Assisted Content Delivery Networks With Wireless Backhaul
Vu, Thang Xuan UL; Chatzinotas, Symeon UL; Ottersten, Björn UL et al

in IEEE Wireless Communications Letters (2018), 7(3), 332-335

Content caching is an efficient technique to reduce delivery latency and system congestion during peak-traffic time by bringing data closer to end users. In this letter, we investigate energy-efficiency ... [more ▼]

Content caching is an efficient technique to reduce delivery latency and system congestion during peak-traffic time by bringing data closer to end users. In this letter, we investigate energy-efficiency performance of cache-assisted content delivery networks with wireless backhaul by taking into account cache capability when designing the signal transmission. We consider multi-layer caching and the performance in cases when both base station and users are capable of storing content data in their local cache. Specifically, we analyze energy consumption in both backhaul and access links under two uncoded and coded caching strategies. Then two optimization problems are formulated to minimize total energy cost for the two caching strategies while satisfying some given quality of service constraint. We demonstrate via numerical results that the uncoded caching achieves higher energy efficiency than the coded caching in the small user cache size regime. [less ▲]

Detailed reference viewed: 95 (2 UL)
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See detailEnergy Minimization for Cache-assisted Content Delivery Networks with Wireless Backhaul
Vu, Thang Xuan UL; Chatzinotas, Symeon UL; Ottersten, Björn UL et al

in IEEE Wireless Communications Letters (2017)

Content caching is an efficient technique to reduce delivery latency and system congestion during peak-traffic time by bringing data closer to end users. In this paper, we investigate energy-efficiency ... [more ▼]

Content caching is an efficient technique to reduce delivery latency and system congestion during peak-traffic time by bringing data closer to end users. In this paper, we investigate energy-efficiency performance of cache-assisted content delivery networks with wireless backhaul by taking into account cache capability when designing the signal transmission. We consider multi-layer caching and the performance in cases when both base station (BS) and users are capable of storing content data in their local cache. Specifically, we analyse energy consumption in both backhaul and access links under two uncoded and coded caching strategies. Then two optimization problems are formulated to minimize total energy cost for the two caching strategies while satisfying some given quality of service constraint. We demonstrate via numerical results that the uncoded caching achieves higher energy efficiency than the coded caching in the small user cache size regime. [less ▲]

Detailed reference viewed: 144 (5 UL)