Joint Client Scheduling and Resource Allocation Under Channel Uncertainty in Federated Learning

Wadu, Madhusanka Manimel; Samarakoon, Sumudu; Bennis, Mehdi

doi:10.1109/tcomm.2021.3088528

Cited by 46 publications

(20 citation statements)

References 38 publications

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“…With a fixed training time budget, a joint bandwidth allocation and scheduling policy was proposed in [24]. By considering imperfect CSI, the authors in [25] proposed a joint device scheduling and resource allocation algorithm to improve the training performance. Taking into account the CPU-GPU heterogeneous computing, the authors in [26] designed a joint computation and communication resource allocation scheme to enhance the energy-efficiency of FL.…”

Section: B Related Workmentioning

confidence: 99%

Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning

Zou¹,

Wang²,

Chen³

et al. 2022

Preprint

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In this paper, we consider communication-efficient over-the-air federated learning (FL), where multiple edge devices with non-independent and identically distributed datasets perform multiple local iterations in each communication round and then concurrently transmit their updated gradients to an edge server over the same radio channel for global model aggregation using over-the-air computation (AirComp). We derive the upper bound of the time-average norm of the gradients to characterize the convergence of AirComp-assisted FL, which reveals the impact of the model aggregation errors accumulated over all communication rounds on convergence. Based on the convergence analysis, we formulate an optimization problem to minimize the upper bound to enhance the learning performance, followed by proposing an alternating optimization algorithm to facilitate the optimal transceiver design for AirCompassisted FL. As the alternating optimization algorithm suffers from high computation complexity, we further develop a knowledge-guided learning algorithm that exploits the structure of the analytic expression of the optimal transmit power to achieve computation-efficient transceiver design. Simulation results demonstrate that the proposed knowledge-guided learning algorithm achieves a comparable performance as the alternating optimization algorithm, but with a much lower computation complexity. Moreover, both proposed algorithms outperform the baseline methods in terms of convergence speed and test accuracy.

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Section: B Related Workmentioning

confidence: 99%

Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning

Zou¹,

Wang²,

Chen³

et al. 2022

Preprint

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“…Another line of work on OTA with imperfect CSI related to this paper is based on device scheduling design. For example, a dynamic energyaware scheduling algorithm was proposed in [11] by taking computation energy constraint into account, while resource allocation with client scheduling was also considered in [12].…”

Section: Related Workmentioning

confidence: 99%

CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networks

Mao¹,

Yang²,

Qiu³

et al. 2022

Preprint

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Over-the-air federated learning (OTA-FL) has emerged as an efficient mechanism that exploits the superposition property of the wireless medium and performs model aggregation for federated learning in the air. OTA-FL is naturally sensitive to wireless channel fading, which could significantly diminish its learning accuracy. To address this challenge, in this paper, we propose an OTA-FL algorithm called CHARLES (channelquality-aware over-the-air local estimating and scaling). Our CHARLES algorithm performs channel state information (CSI) estimation and adaptive scaling to mitigate the impacts of wireless channel fading. We establish the theoretical convergence rate performance of CHARLES and analyze the impacts of CSI error on the convergence of CHARLES. We show that the adaptive channel inversion scaling scheme in CHARLES is robust under imperfect CSI scenarios. We also demonstrate through numerical results that CHARLES outperforms existing OTA-FL algorithms with heterogeneous data under imperfect CSI.• We conduct experiments using convolutional neural network (CNN) on non-i.i.d. MNIST datasets to evaluate the

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“…3) Stochastic Optimization: In large-scale edge AI systems, the estimated CSI will be inevitably imperfect or partially available [271], [177]. It is thus critical to design practical resource allocation schemes by considering the CSI uncertainty, for which robust optimization and stochastic optimization are two typical approaches.…”

Section: ) Mixed-combinatorial Optimizationmentioning

confidence: 99%

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

Letaief¹,

Shi²,

Lu³

et al. 2021

Preprint

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The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from "connected things" to "connected intelligence". However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion, and privacy leakage in both of the training and inference processes. By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence, thereby improving the efficiency, effectiveness, privacy, and security of 6G networks. In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described. Standardization, software and hardware platforms, and application scenarios are also discussed to facilitate the industrialization and commercialization of edge AI systems.

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Joint Client Scheduling and Resource Allocation Under Channel Uncertainty in Federated Learning

Cited by 46 publications

References 38 publications

Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning

Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning

CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networks

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

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