FedBWO: Enhancing Communication Efficiency in Federated Learning

• URL: http://arxiv.org/abs/2505.04435v1
• Date: Wed, 07 May 2025 14:02:35 GMT
• Title: FedBWO: Enhancing Communication Efficiency in Federated Learning
• Authors: Vahideh Hayyolalam, Öznur Özkasap,
• Abstract summary: Federated Learning (FL) is a distributed Machine Learning (ML) setup, where a shared model is collaboratively trained by various clients using their local datasets while keeping the data private.<n>Considering resource constraints, increasing the number of clients and the amount of data (model weights) can lead to a bottleneck.<n>In this paper, we introduce the Federated Black Widow Optimization (FedBWO) technique to decrease the amount of transmitted data by transmitting only a performance score rather than the local model weights from clients.
• Score: 4.788163807490197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) is a distributed Machine Learning (ML) setup, where a shared model is collaboratively trained by various clients using their local datasets while keeping the data private. Considering resource-constrained devices, FL clients often suffer from restricted transmission capacity. Aiming to enhance the system performance, the communication between clients and server needs to be diminished. Current FL strategies transmit a tremendous amount of data (model weights) within the FL process, which needs a high communication bandwidth. Considering resource constraints, increasing the number of clients and, consequently, the amount of data (model weights) can lead to a bottleneck. In this paper, we introduce the Federated Black Widow Optimization (FedBWO) technique to decrease the amount of transmitted data by transmitting only a performance score rather than the local model weights from clients. FedBWO employs the BWO algorithm to improve local model updates. The conducted experiments prove that FedBWO remarkably improves the performance of the global model and the communication efficiency of the overall system. According to the experimental outcomes, FedBWO enhances the global model accuracy by an average of 21% over FedAvg, and 12% over FedGWO. Furthermore, FedBWO dramatically decreases the communication cost compared to other methods.

Related papers

• FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation [22.281467168796645]
  Federated learning (FL) is a collaborative machine learning approach that enables multiple clients to train models without sharing their private data.<n>We propose FedMoE-DA, a new FL model training framework that incorporates a novel domain-aware, fine-grained aggregation strategy to enhance the robustness, personalizability, and communication efficiency simultaneously.
  arXiv  Detail & Related papers  (2024-11-04T14:29:04Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning [0.5510212613486574]
  Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients. Recent works on designing systems for efficient FL have shown that utilizing serverless computing technologies can enhance resource efficiency, reduce training costs, and alleviate the complex infrastructure management burden on data holders.
  arXiv  Detail & Related papers  (2024-02-11T20:15:52Z)
• FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy [7.963276533979389]
  Federated Learning (FL) achieves great popularity in the Internet of Things (IoT) We present FLrce, an efficient FL framework with a relationship-based client selection and early-stopping strategy. Experiment results show that, compared with existing efficient FL frameworks, FLrce improves the computation and communication efficiency by at least 30% and 43% respectively.
  arXiv  Detail & Related papers  (2023-10-15T10:13:44Z)
• Towards Instance-adaptive Inference for Federated Learning [80.38701896056828]
  Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. In this paper, we present a novel FL algorithm, i.e., FedIns, to handle intra-client data heterogeneity by enabling instance-adaptive inference in the FL framework. Our experiments show that our FedIns outperforms state-of-the-art FL algorithms, e.g., a 6.64% improvement against the top-performing method with less than 15% communication cost on Tiny-ImageNet.
  arXiv  Detail & Related papers  (2023-08-11T09:58:47Z)
• Optimizing the Collaboration Structure in Cross-Silo Federated Learning [43.388911479025225]
  In federated learning (FL), multiple clients collaborate to train machine learning models together. We propose FedCollab, a novel FL framework that alleviates negative transfer by clustering clients into non-overlapping coalitions. Our results demonstrate that FedCollab effectively mitigates negative transfer across a wide range of FL algorithms and consistently outperforms other clustered FL algorithms.
  arXiv  Detail & Related papers  (2023-06-10T18:59:50Z)
• 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)
• Acceleration of Federated Learning with Alleviated Forgetting in Local Training [61.231021417674235]
  Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy. We propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage. Our experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep.
  arXiv  Detail & Related papers  (2022-03-05T02:31:32Z)
• Federated Learning over Wireless IoT Networks with Optimized Communication and Resources [98.18365881575805]
  Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention. It is of interest to investigate fast responding and accurate FL schemes over wireless systems. We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
  arXiv  Detail & Related papers  (2021-10-22T13:25:57Z)
• Dynamic Attention-based Communication-Efficient Federated Learning [85.18941440826309]
  Federated learning (FL) offers a solution to train a global machine learning model. FL suffers performance degradation when client data distribution is non-IID. We propose a new adaptive training algorithm $textttAdaFL$ to combat this degradation.
  arXiv  Detail & Related papers  (2021-08-12T14:18:05Z)

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.