Edge Learning via Federated Split Decision Transformers for Metaverse Resource Allocation

Fatih Temiz; Shavbo Salehi; Melike Erol-Kantarci

cs.NIFeb 18, 2026

Edge Learning via Federated Split Decision Transformers for Metaverse Resource Allocation

Authors: Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci

Organizations: School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

Abstract

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

RAT/Clutter	BW	$f_{c}$	PLE	$\sigma$	Dist.	$P$
	[MHz]	[GHz]	$\alpha$	[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	$\gamma_{q}^{\text{cpu}}$ ( $q$ =1,2,3)	200, 190, 180
n_blocks	6	$F$ , $M$	16, 16
context_length	50	$T_{th}$ (ms), $QoE_{\min}$	200, 4
optimizer	AdamW	$C_{r}$ , $NF$ (dB)	300, 7
learning_rate	$10^{-4}$	$f^{\text{CPU}}$ (cyc/s), $f^{\text{GPU}}$ (px/s)	$15\times 10^{9}$
weight_decay	$10^{-4}$	$w_{fair}$ , $w_{viol}$	2, 2

TABLE III: FSDT and MEC parameters.

Fig. 2: Average reward versus episode for baselines and FSDT

Metric	Decoder (Cloud)	Embed (Edge)	Pred (Edge)
# of Params	4,738,560	42,496	15,677
Size (MB)	18.076	0.162	0.060
Memory (%)	98.79	0.89	0.33

TABLE IV: FSDT Split Details

Wireless Resource Allocation

Wireless resource allocation assigns spectrum, power, beams, and computing capacity to meet throughput, latency, fairness, and energy goals.

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May 18, 2026J/K move · Enter open · S save

Heterogeneous Tasks Offloading in Vehicular Edge Computing: A Federated Meta Deep Reinforcement Learning Approach

Yaorong Huang, Jingtao Luo, Xuechao Wang

∗The Hong Kong University of Science and Technology (Guangzhou) · †Chengdu Neusoft University

Federated RL Edge Computing Combinatorial Optimization Heterogeneous Computing Meta-Reinforcement Learning

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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.

Abstract

Wireless Resource Allocation

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