Federated learning over physical channels: adaptive algorithms with near-optimal guarantees

• URL: http://arxiv.org/abs/2509.02538v1
• Date: Tue, 02 Sep 2025 17:40:27 GMT
• Title: Federated learning over physical channels: adaptive algorithms with near-optimal guarantees
• Authors: Rui Zhang, Wenlong Mou,
• Abstract summary: In federated learning, communication cost can be significantly reduced by transmitting the information over the air through physical channels.<n>We propose a new class of adaptive federated gradient descent (SGD) algorithms that can be implemented over physical channels, taking into account both channel noise and hardware constraints.
• Score: 5.881472978395745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In federated learning, communication cost can be significantly reduced by transmitting the information over the air through physical channels. In this paper, we propose a new class of adaptive federated stochastic gradient descent (SGD) algorithms that can be implemented over physical channels, taking into account both channel noise and hardware constraints. We establish theoretical guarantees for the proposed algorithms, demonstrating convergence rates that are adaptive to the stochastic gradient noise level. We also demonstrate the practical effectiveness of our algorithms through simulation studies with deep learning models.

Related papers

• Over-the-Air Federated Learning via Weighted Aggregation [9.043019524847491]
  This paper introduces a new federated learning scheme that leverages over-the-air computation. A novel feature of this scheme is the proposal to employ adaptive weights during aggregation. We provide a mathematical methodology to derive the convergence bound for the proposed scheme.
  arXiv  Detail & Related papers  (2024-09-12T08:07:11Z)
• Adaptive Federated Learning Over the Air [108.62635460744109]
  We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. Our analysis shows that the AdaGrad-based training algorithm converges to a stationary point at the rate of $mathcalO( ln(T) / T 1 - frac1alpha ).
  arXiv  Detail & Related papers  (2024-03-11T09:10:37Z)
• Over-the-Air Federated Learning and Optimization [52.5188988624998]
  We focus on Federated learning (FL) via edge-the-air computation (AirComp) We describe the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both convex and non- convex settings. For different types of local updates that can be transmitted by edge devices (i.e., model, gradient, model difference), we reveal that transmitting in AirFedAvg may cause an aggregation error. In addition, we consider more practical signal processing schemes to improve the communication efficiency and extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes.
  arXiv  Detail & Related papers  (2023-10-16T05:49:28Z)
• Stochastic Unrolled Federated Learning [85.6993263983062]
  We introduce UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning. Our proposed method tackles two challenges of this expansion, namely the need to feed whole datasets to the unrolleds and the decentralized nature of federated learning.
  arXiv  Detail & Related papers  (2023-05-24T17:26:22Z)
• Training neural networks with structured noise improves classification and generalization [0.0]
  We show how adding structure to noisy training data can substantially improve the algorithm performance. We also prove that the so-called Hebbian Unlearning rule coincides with the training-with-noise algorithm when noise is maximal.
  arXiv  Detail & Related papers  (2023-02-26T22:10:23Z)
• Faster Adaptive Federated Learning [84.38913517122619]
  Federated learning has attracted increasing attention with the emergence of distributed data. In this paper, we propose an efficient adaptive algorithm (i.e., FAFED) based on momentum-based variance reduced technique in cross-silo FL.
  arXiv  Detail & Related papers  (2022-12-02T05:07:50Z)
• Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics [96.9177297872723]
  We present a novel method for guaranteeing linear momentum in learned physics simulations. We enforce conservation of momentum with a hard constraint, which we realize via antisymmetrical continuous convolutional layers. In combination, the proposed method allows us to increase the physical accuracy of the learned simulator substantially.
  arXiv  Detail & Related papers  (2022-10-12T09:12:59Z)
• Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning [28.65635956111857]
  Federated learning (FL) enables edge devices to collaboratively train a global model without sharing their private data. To enhance the training efficiency of FL, various algorithms have been proposed, ranging from first-order computation to first-order methods.
  arXiv  Detail & Related papers  (2022-01-24T08:56:06Z)
• Self-Guided Quantum State Learning for Mixed States [7.270980742378388]
  The salient features of our algorithm are efficient $O left( d3 right)$ post-processing in the infidelity dimension $d$ of the state. A higher resilience against measurement noise makes our algorithm suitable for noisy intermediate-scale quantum applications.
  arXiv  Detail & Related papers  (2021-06-11T04:40:26Z)
• Fast Convergence Algorithm for Analog Federated Learning [30.399830943617772]
  We propose an AirComp-based FedSplit algorithm for efficient analog federated learning over wireless channels. We prove that the proposed algorithm linearly converges to the optimal solutions under the assumption that the objective function is strongly convex and smooth. Our algorithm is theoretically and experimentally verified to be much more robust to the ill-conditioned problems with faster convergence compared with other benchmark FL algorithms.
  arXiv  Detail & Related papers  (2020-10-30T10:59:49Z)
• Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications [54.610318402371185]
  Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
  arXiv  Detail & Related papers  (2020-05-25T01:42:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.