Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems

• URL: http://arxiv.org/abs/2506.22374v1
• Date: Fri, 27 Jun 2025 16:41:23 GMT
• Title: Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems
• Authors: Abdulmomen Ghalkha, Zhuojun Tian, Chaouki Ben Issaid, Mehdi Bennis,
• Abstract summary: We propose Sheaf-DMFL, a novel decentralized multimodal learning framework to enhance collaboration among devices with diverse modalities.<n>We also propose an enhanced algorithm named Sheaf-DMFL-Att, which tailors the attention mechanism within each client to capture correlations among different modalities.
• Score: 32.21609864602662
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In large-scale communication systems, increasingly complex scenarios require more intelligent collaboration among edge devices collecting various multimodal sensory data to achieve a more comprehensive understanding of the environment and improve decision-making accuracy. However, conventional federated learning (FL) algorithms typically consider unimodal datasets, require identical model architectures, and fail to leverage the rich information embedded in multimodal data, limiting their applicability to real-world scenarios with diverse modalities and varying client capabilities. To address this issue, we propose Sheaf-DMFL, a novel decentralized multimodal learning framework leveraging sheaf theory to enhance collaboration among devices with diverse modalities. Specifically, each client has a set of local feature encoders for its different modalities, whose outputs are concatenated before passing through a task-specific layer. While encoders for the same modality are trained collaboratively across clients, we capture the intrinsic correlations among clients' task-specific layers using a sheaf-based structure. To further enhance learning capability, we propose an enhanced algorithm named Sheaf-DMFL-Att, which tailors the attention mechanism within each client to capture correlations among different modalities. A rigorous convergence analysis of Sheaf-DMFL-Att is provided, establishing its theoretical guarantees. Extensive simulations are conducted on real-world link blockage prediction and mmWave beamforming scenarios, demonstrate the superiority of the proposed algorithms in such heterogeneous wireless communication systems.

Related papers

• Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning [11.830276582141096]
  In complex network topologies with multiple users, the combinations of client data and channel state information (CSI) pose significant challenges for existing semantic communication models.<n>We propose a novel personalized semantic communication models based on channel awareness model.<n>Within this framework, we present a framework that achieves the zero optimization gap for non bandwidth-related loss functions.
  arXiv  Detail & Related papers  (2025-03-18T10:02:22Z)
• Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach [37.4602828056364]
  We introduce a novel sheaf-theoretic-based approach for Federated Multi-task Learning (FMTL)<n>By representing client relationships using cellular sheaves, our framework can flexibly model interactions between heterogeneous client models.<n>We show that our proposed algorithm, Sheaf-FMTL, achieves a sublinear convergence rate in line with state-of-the-art decentralized FMTL algorithms.
  arXiv  Detail & Related papers  (2025-02-03T08:25:34Z)
• DeepInteraction++: Multi-Modality Interaction for Autonomous Driving [80.8837864849534]
  We introduce a novel modality interaction strategy that allows individual per-modality representations to be learned and maintained throughout.<n>DeepInteraction++ is a multi-modal interaction framework characterized by a multi-modal representational interaction encoder and a multi-modal predictive interaction decoder.<n>Experiments demonstrate the superior performance of the proposed framework on both 3D object detection and end-to-end autonomous driving tasks.
  arXiv  Detail & Related papers  (2024-08-09T14:04:21Z)
• Balancing Similarity and Complementarity for Federated Learning [91.65503655796603]
  Federated Learning (FL) is increasingly important in mobile and IoT systems. One key challenge in FL is managing statistical heterogeneity, such as non-i.i.d. data. We introduce a novel framework, textttFedSaC, which balances similarity and complementarity in FL cooperation.
  arXiv  Detail & Related papers  (2024-05-16T08:16:19Z)
• FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients [32.59184269562571]
  We propose a multi-modal collaborative diffusion federated learning framework called FedDiff. Our framework establishes a dual-branch diffusion model feature extraction setup, where the two modal data are inputted into separate branches of the encoder. Considering the challenge of private and efficient communication between multiple clients, we embed the diffusion model into the federated learning communication structure.
  arXiv  Detail & Related papers  (2023-11-16T02:29:37Z)
• Rate-Adaptive Coding Mechanism for Semantic Communications With Multi-Modal Data [23.597759255020296]
  We propose a distributed multi-modal semantic communication framework incorporating the conventional channel encoder/decoder. We establish a general rate-adaptive coding mechanism for various types of multi-modal semantic tasks. Numerical results show that the proposed mechanism fares better than both conventional communication and existing semantic communication systems.
  arXiv  Detail & Related papers  (2023-05-18T07:31:37Z)
• Personalizing Federated Learning with Over-the-Air Computations [84.8089761800994]
  Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. This paper presents a distributed training paradigm that employs analog over-the-air computation to address the communication bottleneck.
  arXiv  Detail & Related papers  (2023-02-24T08:41:19Z)
• Federated Gradient Matching Pursuit [17.695717854068715]
  Traditional machine learning techniques require centralizing all training data on one server or data hub. In particular, federated learning (FL) provides such a solution to learn a shared model while keeping training data at local clients. We propose a novel algorithmic framework, federated gradient matching pursuit (FedGradMP), to solve the sparsity constrained minimization problem in the FL setting.
  arXiv  Detail & Related papers  (2023-02-20T16:26:29Z)
• FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning [87.08902493524556]
  Federated learning(FL) has recently attracted increasing attention from academia and industry. We propose FedDM to build the global training objective from multiple local surrogate functions. In detail, we construct synthetic sets of data on each client to locally match the loss landscape from original data.
  arXiv  Detail & Related papers  (2022-07-20T04:55:18Z)
• Dif-MAML: Decentralized Multi-Agent Meta-Learning [54.39661018886268]
  We propose a cooperative multi-agent meta-learning algorithm, referred to as MAML or Dif-MAML. We show that the proposed strategy allows a collection of agents to attain agreement at a linear rate and to converge to a stationary point of the aggregate MAML. Simulation results illustrate the theoretical findings and the superior performance relative to the traditional non-cooperative setting.
  arXiv  Detail & Related papers  (2020-10-06T16:51:09Z)
• Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles [52.79089414630366]
  We develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL) We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner.
  arXiv  Detail & Related papers  (2020-07-27T12:38:37Z)

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.