Mobile edge computing (MEC) based wireless metaverse services offer an untethered, immersive experience to users, where the superior quality of experience (QoE) needs to be achieved under stringent latency constraints and visual quality demands. To achieve this, MEC-based intelligent resource allocation for virtual reality users needs to be supported by coordination across MEC servers to harness distributed data. Federated learning (FL) is a promising solution, and can be combined with reinforcement learning (RL) to develop generalized policies across MEC-servers. However, conventional FL incurs transmitting the full model parameters across the MEC-servers and the cloud, and suffer performance degradation due to naive global aggregation, especially in heterogeneous multi-radio access technology environments. To address these challenges, this paper proposes Federated Split Decision Transformer (FSDT), an offline RL framework where the transformer model is partitioned between MEC servers and the cloud. Agent-specific components (e.g., MEC-based embedding and prediction layers) enable local adaptability, while shared global layers in the cloud facilitate cooperative training across MEC servers. Experimental results demonstrate that FSDT enhances QoE for up to 10% in heterogeneous environments compared to baselines, while offloadingnearly 98% of the transformer model parameters to the cloud, thereby reducing the computational burden on MEC servers.
Figures & tables
Fig. 1: System Model
RAT/Clutter
BW
fc
PLE
σ
Dist.
P
[MHz]
[GHz]
α
[dB]
[m]
[dBm]
UMB/UMi
200
6.75
2.56
6.53
230
33
UMB/InH
200
6.75
2.72
9.21
65
33
Sub6GHz/UMi
100
2.90
2.90
2.90
230
30
Sub6GHz/InH
100
2.90
3.10
6.50
65
30
Wi-Fi/InH
160
2.40
2.52
5.75
65
24
TABLE I: multi-RAT Parameters
Algorithm
Train. Mode
Aggregation
Exchanged Info.
C-DDPG [ 5 ]
Centralized
N/A
N/A
F-DDPG
Federated
All layers
Critic Params
C-DT [ 6 ]
Centralized
N/A
N/A
F-DT
Federated
All layers
Full Params
FSDT
Fed.-Split
Local layers
Embeddings
TABLE II: Comparison of baselines and the proposed FSDT.
FSDT parameter
Value
MEC parameter
Value
n_heads
4
γqcpu ( q =1,2,3)
200, 190, 180
n_blocks
6
F , M
16, 16
context_length
50
Tth (ms), QoEmin
200, 4
optimizer
AdamW
Cr , NF (dB)
300, 7
learning_rate
10−4
fCPU (cyc/s), fGPU (px/s)
15×109
weight_decay
10−4
wfair , wviol
2, 2
TABLE III: FSDT and MEC parameters.
Fig. 2: Average reward versus episode for baselines and FSDT
Vehicular edge computing (VEC) enables latency-sensitive vehicular applications by offloading computation-intensive tasks to nearby edge servers. However, real-world vehicular workloads are typically modeled as heterogeneous directed acyclic graph (DAG) tasks with complex dependency structures, making joint offloading and resource allocation highly challenging. Moreover, distributed MEC deployment raises privacy concerns when collaboratively training learning-based policies. In this paper, we propose a Federated Meta Deep Reinforcement Learning framework with GAT-Seq2Seq modeling (FedMAGS) for heterogeneous task offloading in VEC systems. The proposed approach leverages Graph Attention Networks to capture DAG dependencies, a Seq2Seq-based policy to generate structured offloading decisions, and federated meta-learning to enable fast adaptation across distributed MEC servers without sharing raw data. Extensive simulations demonstrate that FedMAGS achieves faster convergence, lower execution delay, and better scalability compared with state-of-the-art baselines. In addition, the federated design preserves data privacy while reducing communication overhead, making the framework well suited for dynamic and large-scale VEC environments.
Yaorong Huang, Jingtao Luo, Xuechao Wang
∗The Hong Kong University of Science and Technology (Guangzhou) · †Chengdu Neusoft University
Federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing their raw data. However, the limited computation resources of the clients may result in a high delay and energy consumption on training. In this paper, we propose an adaptive split federated learning (ASFL) framework over wireless networks. ASFL exploits the computation resources of the central server to train part of the model and enables adaptive model splitting as well as resource allocation during training. To optimize the learning performance (i.e., convergence rate) and efficiency (i.e., delay and energy consumption) of ASFL, we theoretically analyze the convergence rate and formulate a joint learning performance and resource allocation optimization problem. Solving this problem is challenging due to the long-term delay and energy consumption constraints as well as the coupling of the model splitting and resource allocation decisions. We propose an online optimization enhanced block coordinate descent (OOE-BCD) algorithm to solve the problem iteratively. Experimental results show that when compared with five baseline schemes, our proposed ASFL framework converges faster and reduces the total delay and energy consumption by up to 75% and 80%, respectively.
Chuiyang Meng, Ming Tang, Vincent W. S. Wong
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada · Department of Computer Science and Engineering, Southern University of Science and Technology, China
Split federated learning (SFL) has emerged as a powerful paradigm for model training at the edge. However, SFL inherently involves discrete decision variables for model splitting and resource allocation, resulting in a challenging mixed-integer problem. Consequently, prior optimization schemes for SFL are either heuristic or computationally inefficient, which cannot handle large-scale user populations. To address this limitation, this work establishes an efficient optimization framework for SFL under resource-constrained networks. Our framework jointly optimizes model splitting and resource allocation to minimize training cost, which is defined as the weighted sum of latency and energy costs. We first study the model splitting problem and develop a polynomial-time algorithm that achieves the global optimum. Then, we extend the approach to the joint model splitting and resource allocation problem. In this case, we formulate it as a two-dimensional master problem and develop an efficient approximation method with a (1+ε)-approximation guarantee. Extensive experiments show that the proposed approach provides efficient solutions to strike the optimal energy--latency tradeoff.
Wei Wei, Xianhao Chen
Department of Electrical and Computer Engineering, the University of Hong Kong, Pok Fu Lam, Hong Kong SAR, 999077