Edge Learning via Federated Split Decision Transformers for Metaverse Resource Allocation

• URL: http://arxiv.org/abs/2602.16174v1
• Date: Wed, 18 Feb 2026 04:19:57 GMT
• Title: Edge Learning via Federated Split Decision Transformers for Metaverse Resource Allocation
• Authors: Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci,
• Abstract summary: This paper proposes Federated Split Decision Transformer (FSDT), an offline RL framework where the transformer model is partitioned between MEC servers and the cloud.<n> Experimental results demonstrate that FSDT enhances QoE for up to 10% in heterogeneous environments compared to baselines.
• Score: 3.8613477927967614
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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