Federated Distributionally Robust Optimization for Phase Configuration of RISs

• URL: http://arxiv.org/abs/2108.09026v1
• Date: Fri, 20 Aug 2021 07:07:45 GMT
• Title: Federated Distributionally Robust Optimization for Phase Configuration of RISs
• Authors: Chaouki Ben Issaid, Sumudu Samarakoon, Mehdi Bennis, and H. Vincent Poor
• Abstract summary: We study the problem of robust reconfigurable intelligent surface (RIS)-aided downlink communication over heterogeneous RIS types in a supervised learning setting. By modeling downlink communication over heterogeneous RIS designs as different workers that learn how to optimize phase configurations in a distributed manner, we solve this distributed learning problem. Our proposed algorithm requires fewer communication rounds to achieve the same worst-case distribution test accuracy compared to competitive baselines.
• Score: 106.4688072667105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this article, we study the problem of robust reconfigurable intelligent surface (RIS)-aided downlink communication over heterogeneous RIS types in the supervised learning setting. By modeling downlink communication over heterogeneous RIS designs as different workers that learn how to optimize phase configurations in a distributed manner, we solve this distributed learning problem using a distributionally robust formulation in a communication-efficient manner, while establishing its rate of convergence. By doing so, we ensure that the global model performance of the worst-case worker is close to the performance of other workers. Simulation results show that our proposed algorithm requires fewer communication rounds (about 50% lesser) to achieve the same worst-case distribution test accuracy compared to competitive baselines.

Related papers

• Statistical Inference for Conditional Group Distributionally Robust Optimization with Cross-Entropy Loss [9.054486124506521]
  We study multi-source unsupervised domain adaptation, where labeled data are drawn from multiple source domains and only unlabeled data from a target domain.<n>We propose a novel Conditional Conditional Optimization (CG-DRO) framework that learns a classifier by minimizing the worst-case cross-entropy loss over the convex combinations of the conditional outcome distributions from the sources.<n>We establish fast statistical convergence rates for the estimator by constructing two surrogate minimax optimization problems that serve as theoretical bridges.
  arXiv  Detail & Related papers  (2025-07-14T04:21:23Z)
• Distributionally Robust Federated Learning with Client Drift Minimization [35.08453461129848]
  textitDRDM is a distributionally robust optimization framework with dynamic regularization to mitigate client drift.<n>textitDRDM frames the training as a min-max optimization problem aimed at maximizing performance for the worst-case client.<n>Experiments show that textitDRDM significantly improves worst-case test accuracy while requiring fewer communication rounds.
  arXiv  Detail & Related papers  (2025-05-21T11:05:56Z)
• Preference Optimization for Combinatorial Optimization Problems [54.87466279363487]
  Reinforcement Learning (RL) has emerged as a powerful tool for neural optimization, enabling models learns that solve complex problems without requiring expert knowledge.<n>Despite significant progress, existing RL approaches face challenges such as diminishing reward signals and inefficient exploration in vast action spaces.<n>We propose Preference Optimization, a novel method that transforms quantitative reward signals into qualitative preference signals via statistical comparison modeling.
  arXiv  Detail & Related papers  (2025-05-13T16:47:00Z)
• Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls [8.720733751119994]
  We study the underlying optimization problem, develop efficient solution algorithms, and demonstrate that the proposed method outperforms benchmark approaches on standard datasets.<n>Inspired by the former, we study the Wasserstein DR counterpart of ARO for logistic regression and show it admits a tractable convex optimization reformulation.
  arXiv  Detail & Related papers  (2024-07-18T15:59:37Z)
• Federated Distributionally Robust Optimization with Non-Convex Objectives: Algorithm and Analysis [24.64654924173679]
  Asynchronous distributed algorithm named Asynchronous Single-looP alternatIve gRadient projEction is proposed. New uncertainty set, i.e., constrained D-norm uncertainty set, is developed to leverage the prior distribution and flexibly control the degree of robustness. empirical studies on real-world datasets demonstrate that the proposed method can not only achieve fast convergence, but also remain robust against data as well as malicious attacks.
  arXiv  Detail & Related papers  (2023-07-25T01:56:57Z)
• Compressed Regression over Adaptive Networks [58.79251288443156]
  We derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem. We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned online by the agents.
  arXiv  Detail & Related papers  (2023-04-07T13:41:08Z)
• Hedging Complexity in Generalization via a Parametric Distributionally Robust Optimization Framework [18.6306170209029]
  Empirical risk minimization (ERM) and distributionally robust optimization (DRO) are popular approaches for solving optimization problems. We propose a simple approach in which the distribution of random perturbations is approximated using a parametric family of distributions. We show that this new source of error can be controlled by suitable DRO formulations.
  arXiv  Detail & Related papers  (2022-12-03T03:26:34Z)
• Distributed Distributionally Robust Optimization with Non-Convex Objectives [24.64654924173679]
  Asynchronous distributed algorithm named Asynchronous Single-looP alternatIve gRadient projEction is proposed. New uncertainty set, i.e., constrained D-norm uncertainty set, is developed to leverage the prior distribution and flexibly control the degree of robustness. empirical studies on real-world datasets demonstrate that the proposed method can not only achieve fast convergence, but also remain robust against data as well as malicious attacks.
  arXiv  Detail & Related papers  (2022-10-14T07:39:13Z)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)
• Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing [88.76112371510999]
  Federated learning is a prime candidate for distributed machine learning at the network edge. Existing algorithms face issues with slow convergence and/or robustness of performance. We propose a contextual aggregation scheme that achieves the optimal context-dependent bound on loss reduction.
  arXiv  Detail & Related papers  (2022-03-23T21:42:31Z)
• Distributionally Robust Learning with Stable Adversarial Training [34.74504615726101]
  Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts. We propose a novel Stable Adversarial Learning (SAL) algorithm that leverages heterogeneous data sources to construct a more practical uncertainty set.
  arXiv  Detail & Related papers  (2021-06-30T03:05:45Z)
• Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations [55.50218493466906]
  In this paper, the problem of robust reconfigurable intelligent surface (RIS) system design under changes in data distributions is investigated. Using the notion of invariant risk minimization (IRM), an invariant causal representation across multiple environments is used such that the predictor is simultaneously optimal for each environment. A neural network-based solution is adopted to seek the predictor and its performance is validated via simulations against an empirical risk minimization-based design.
  arXiv  Detail & Related papers  (2021-05-04T21:36:31Z)
• Stable Adversarial Learning under Distributional Shifts [46.98655899839784]
  Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts. We propose Stable Adversarial Learning (SAL) algorithm that leverages heterogeneous data sources to construct a more practical uncertainty set.
  arXiv  Detail & Related papers  (2020-06-08T08:42:34Z)
• Dynamic Federated Learning [57.14673504239551]
  Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. We consider a federated learning model where at every iteration, a random subset of available agents perform local updates based on their data. Under a non-stationary random walk model on the true minimizer for the aggregate optimization problem, we establish that the performance of the architecture is determined by three factors, namely, the data variability at each agent, the model variability across all agents, and a tracking term that is inversely proportional to the learning rate of the algorithm.
  arXiv  Detail & Related papers  (2020-02-20T15:00:54Z)

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.