As a promising solution to achieve efficient learning among isolated data owners and solve data privacy issues, federated learning is receiving wide attention. Using the edge server as an intermediary can effectively collect sensor data, perform local model training, and upload model parameters for global aggregation. So this paper proposes a new framework for resource allocation in a hierarchical network supported by edge computing. In this framework, we minimize the weighted sum of system cost and learning cost by optimizing bandwidth, computing frequency, power allocation and subcarrier assignment. To solve this challenging mixed-integer non-linear problem, we first decouple the bandwidth optimization problem(P1) from the whole problem and obtain a closed-form solution. The remaining computational frequency, power, and subcarrier joint optimization problem(P2) can be further decomposed into two sub-problems: latency and computational frequency optimization problem(P3) and transmission power and subcarrier optimization problem(P4). P3 is a convex optimization problem that is easy to solve. In the joint optimization problem(P4), the optimal power under each subcarrier selection can be obtained first through the successive convex approximation(SCA) algorithm. Substituting the optimal power value obtained back to P4, the subproblem can be regarded as an assignment problem, so the Hungarian algorithm can be effectively used to solve it. The solution of problem P2 is accomplished by solving P3 and P4 iteratively. To verify the performance of the algorithm, we compare the proposed algorithm with five algorithms; namely Equal bandwidth allocation, Learning cost guaranteed, Greedy subcarrier allocation, System cost guaranteed and Time-biased algorithm. Numerical results show the significant performance gain and the robustness of the proposed algorithm in the face of parameter changes.
Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systems
Beijing University of Posts and Telecommunications（北京邮电大学）；Princeton University（普林斯顿大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Robust Resource Allocation in RIS-Assisted Wireless Networks Integrating NOMA and Over-the-Air Federated Learning
Laval University（拉瓦尔大学）；Ferdowsi University（费尔多西大学）；Henan Polytechnic University（河南理工大学）；Central China Normal University（华中师范大学）；Beijing Jiaotong University（北京交通大学）
Fair Resource Allocation For Hierarchical Federated Edge Learning in Space-Air-Ground Integrated Networks via Deep Reinforcement Learning with Hybrid Control
Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation
Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)（深圳未来智能网络研究院）；School of Science and Engineering (SSE)（科学与工程学院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Advanced Technology (SIAT)（深圳先进技术研究院）；Chinese Academy of Sciences (CAS)（中国科学院）；Southern University of Science and Technology（南方科技大学）；Imperial College London（伦敦帝国学院）；Central South University（中南大学）；Shenzhen University of Advanced Technology（深圳理工大学）
Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms
Virginia Tech（弗吉尼亚理工大学）；University of Colorado Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）；Ericsson Research（爱立信研究院）；Princeton University（普林斯顿大学）；University of Oulu（奥卢大学）
Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks
Beijing University of Posts and Telecommunications（北京邮电大学）；Princeton University（普林斯顿大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Virginia Tech（弗吉尼亚理工大学）；Kyung Hee University（庆熙大学）
Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation
Beijing University of Posts and Telecommunications（北京邮电大学）；North China Electric Power University（华北电力大学）；Commonwealth Science and Industrial Research Organisation（联邦科学与工业研究组织）
