Latency Minimization for TDMA-Based Wireless Federated Learning Networks

Wireless federated learning (FL) is a new distributed machine learning framework that trains a global model through user collaboration over wireless networks. However, the resource heterogeneity of different users greatly impacts on the performance of wireless FL networks. To tackle this challenge,...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on vehicular technology Ročník 73; číslo 9; s. 13974 - 13979
Hlavný autor: Xu, Ding
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.09.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Computational geometry
Computational modeling
Constraints
Convexity
Data models
Energy consumption
Federated learning
Heterogeneity
latency minimization
Machine learning
Network latency
Optimization
resource heterogeneity
Resource management
Scheduling
Time Division Multiple Access
Time-frequency analysis
Training
Wireless communication
Wireless federated learning
Wireless networks
ISSN:0018-9545, 1939-9359
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Wireless federated learning (FL) is a new distributed machine learning framework that trains a global model through user collaboration over wireless networks. However, the resource heterogeneity of different users greatly impacts on the performance of wireless FL networks. To tackle this challenge, this paper proposes a novel time division multiple access (TDMA) based scheme for wireless FL networks, where local model training and model transmission of different users can be performed in parallel. The problem of minimizing the total latency per training round by jointly optimizing user scheduling, time allocation, computing frequency, and energy allocation, subject to the maximum energy consumption constraint and the maximum computing frequency constraint of each user, is investigated. Based on the particular problem structure, the optimal computing frequency and energy allocation are derived as the functions of time allocation. On this basis, a heuristic user scheduling policy is proposed and the optimal time allocation is obtained via convex optimization. Simulation results demonstrate that the proposed algorithm is of low-complexity and close to optimal. It is shown that the proposed TDMA-based scheme can outperform the state-of-the-art schemes in existing literature.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3389972