cs.ITOct 15, 2026

Uncertainty-Aware Federated Learning for Joint Communication and Computation in XR Systems

Authors: Robert-Jeron Reifert, Hayssam Dahrouj, Alaa Alameer Ahmad, Haris Gacanin, Aydin Sezgin

Organizations: Digital Communication Systems, Ruhr University Bochum, Bochum, Germany. · the Department of Electrical Engineering, University of Sharjah, Sharjah, United Arab Emirates. · Cariad SE, Wolfsburg, Germany. · the Chair for Distributed Signal Processing, RWTH Aachen University, Aachen, Germany.

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Abstract

The convergence of communication and computation, empowered by artificial intelligence (AI) enhanced capabilities, emerges as a key enabler for realizing the promising functionalities of the sixth-generation of communication systems (6G). To this end, this paper investigates an AI-native, robust network, where multiple base stations (BSs) simultaneously serve several extended reality (XR) devices. The paper then presents an innovative distributed deep learning-based approach to simultaneously manage the transmit and computing powers, and computation allocation, amidst uncertainties in both channel and computing states information. Under such an interference network scenario, the paper proposes a robust approach for minimizing the worst-case delay across the served XR devices, subject to computation and power constraints. The paper leverages a deep neural network (DNN) that can be trained either centrally or in a federated manner to map estimated channels and computation requirements to optimized resource allocations per BS. During training, uncertainty samples, reflecting both channel and computing estimation errors, are injected after the DNN output to account for such joint uncertainties. Our results validate the enhanced robust delay performance and scalability of joint uncertainty injection as compared to uncertainty-agnostic methods and state-of-the-art benchmarks, with federated learning-based solutions achieving near-centralized performance while enhancing efficiency, privacy, and adaptability.

Figures & tables

Fig. 1 : Network setup with one central server and global DNN model, 33 multi antenna BSs each serving a number of XR devices accommodating a local DNN model. Devices’ communication, computation, and inter-BS interference links are shown.
Fig. 2 : Proposed DNN architecture with estimated input, resource allocation output, and uncertainty injection. Modifications for the distributed DNN are highlighted in green.
Fig. 3 : Channel knowledge representation for 2 BSs and 5 devices illustrating known and unknown channels.
GeneralCommunicationComputation
Pmax=34P^{\text{max}}=34 dBmW=10W=10 MHzFmax=4.6⋅109F^{\text{max}}=4.6\cdot 10^{9} cycles/s
N=1000N=1000 samplesDkout=7.5⋅104D_{k}^{\text{out}}=7.5\hskip-1.42271pt\cdot\hskip-1.42271pt10^{4} bitsDkin=5⋅104D_{k}^{\text{in}}=5\cdot 10^{4} bits
γ=0.05\gamma=0.05α=0.2\alpha=0.2 RZF factorτ=10−28\tau=10^{-28} , μ=3\mu=3
 σ2=−175\sigma^{2}=-175 dBm/Hzωk∼Γ(2,200)\omega_{k}\sim\Gamma(2,200) , ∀k∈K\forall k\in\mathcal{K}
TABLE I : Simulation parameters (unless otherwise mentioned).
Fig. 4 : Robust delay t^maxγ\hat{t}_{\text{max}}^{\hskip 0.56917pt\gamma} over channel uncertainty, i.e., σh2\sigma_{h}^{2} .
Fig. 5 : Robust delay t^maxγ\hat{t}_{\text{max}}^{\hskip 0.56917pt\gamma} over computing uncertainty, i.e., σω2\sigma_{\omega}^{2} .
Fig. 6 : Robust delay t^maxγ\hat{t}_{\text{max}}^{\hskip 0.56917pt\gamma} over computing uncertainty, i.e., σω2\sigma_{\omega}^{2} .
Fig. 7 : Robust worst-case delay t^maxγ\hat{t}_{\text{max}}^{\hskip 0.56917pt\gamma} vs. number of devices KK .
Fig. 8 : Robust delay t^maxγ\hat{t}_{\text{max}}^{\hskip 0.56917pt\gamma} over network density.
Network densitysparsemoderatedense
Unit in mstinfdnn\bm{t^{\text{dnn}}_{\text{inf}}}t^maxγ\bm{\hat{t}_{\text{max}}^{\hskip 0.56917pt\gamma}}tinfdnn\bm{t^{\text{dnn}}_{\text{inf}}}t^maxγ\bm{\hat{t}_{\text{max}}^{\hskip 0.56917pt\gamma}}tinfdnn\bm{t^{\text{dnn}}_{\text{inf}}}t^maxγ\bm{\hat{t}_{\text{max}}^{\hskip 0.56917pt\gamma}}
Non-robust FL4.49843.7174.30191.9184.525144.651
Robust FL4.40831.3924.19965.3984.288112.939
Non-robust FP8.5 ⋅103\cdot 10^{3}59.84712.2 ⋅103\cdot 10^{3}129.59220.2 ⋅103\cdot 10^{3}212.580
SAA/WMMSE9.6 ⋅103\cdot 10^{3}42.52038.6 ⋅103\cdot 10^{3}96.609104.6 ⋅103\cdot 10^{3}176.476
Uniform FP6.4 ⋅103\cdot 10^{3}42.5829.9 ⋅103\cdot 10^{3}100.18716.9 ⋅103\cdot 10^{3}178.833
TABLE II : Mean inference/optimization time tinfdnn\bm{t^{\text{dnn}}_{\text{inf}}} for ML-based schemes and optimization baselines.
Fig. 9 : Robust worst-case delay t^maxγ\hat{t}_{\text{max}}^{\hskip 0.56917pt\gamma} vs. data heterogeneity for different FL implementations.
Fig. 10 : Robust worst-case delay t^maxγ\hat{t}_{\text{max}}^{\hskip 0.56917pt\gamma} vs. parameter γ\gamma , showing impact of hard latency deadline guarantees.
Fig. 11 : Robust worst-case delay t^maxγ\hat{t}_{\text{max}}^{\hskip 0.56917pt\gamma} vs. three uncertainty levels ({ low , medium , high } corresponding to (σh2,σω2)∈{(0.004,800),(0.008,1600),(0.016,3200)}(\sigma_{h}^{2},\sigma_{\omega}^{2})\in\{(0.004,800),(0.008,1600),(0.016,3200)\} , respectively).
Fig. 12 : Convergence behavior of centralized learning and FL.
Fig. 13 : Robust worst-case delay t^maxγ\hat{t}_{\text{max}}^{\hskip 0.56917pt\gamma} vs. training epochs showing the impact of uncertainty samples NN .
Training Epochs E\mathbf{E}200\mathbf{200}300\mathbf{300}400\mathbf{400}500\mathbf{500}
N=500N=50044296657888511110
N=1000N=10008593129061722221533
TABLE III : Training time in seconds for Robust FL .
Fig. 14 : Exchanged data during training in MegaByte (MB) versus the number of BSs for different device numbers.
Fig. 15 : Exchanged data for inference in kiloByte (kB) versus the number of BSs for different device numbers.
Fig. 16 : Robust worst-case delay t^maxγ\hat{t}_{\text{max}}^{\hskip 0.56917pt\gamma} vs. neurons per hidden layer nhn_{h} (left) and number of hidden layers ηh\eta_{h} (right).
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