Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated Learning

• URL: http://arxiv.org/abs/2107.07233v2
• Date: Sat, 17 Jul 2021 13:15:20 GMT
• Title: Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated Learning
• Authors: Shaashwat Agrawal, Sagnik Sarkar, Mamoun Alazab, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu and Quoc-Viet Pham
• Abstract summary: Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of implementation due to technological limitations, communication overhead, non-IID (independent and identically distributed) data, and privacy concerns. We propose a novel hybrid algorithm, namely genetic clustered FL (Genetic CFL), that clusters edge devices based on the training hyper- parameters and genetically modifies the parameters cluster-wise.
• Score: 4.710427287359642
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of implementation due to technological limitations, communication overhead, non-IID (independent and identically distributed) data, and privacy concerns. Training a ML model over heterogeneous non-IID data highly degrades the convergence rate and performance. The existing traditional and clustered FL algorithms exhibit two main limitations, including inefficient client training and static hyper-parameter utilization. To overcome these limitations, we propose a novel hybrid algorithm, namely genetic clustered FL (Genetic CFL), that clusters edge devices based on the training hyper-parameters and genetically modifies the parameters cluster-wise. Then, we introduce an algorithm that drastically increases the individual cluster accuracy by integrating the density-based clustering and genetic hyper-parameter optimization. The results are bench-marked using MNIST handwritten digit dataset and the CIFAR-10 dataset. The proposed genetic CFL shows significant improvements and works well with realistic cases of non-IID and ambiguous data.

Related papers

• Hyperdimensional Computing Empowered Federated Foundation Model over Wireless Networks for Metaverse [56.384390765357004]
  We propose an integrated federated split learning and hyperdimensional computing framework for emerging foundation models. This novel approach reduces communication costs, computation load, and privacy risks, making it suitable for resource-constrained edge devices in the Metaverse.
  arXiv  Detail & Related papers  (2024-08-26T17:03:14Z)
• Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approach [27.86468387141422]
  Federated Edge Learning (FEEL) emerges as a pioneering distributed machine learning paradigm for the 6G Hyper-Connectivity. Current FEEL algorithms struggle with non-independent and non-identically distributed (non-IID) data, leading to elevated communication costs and compromised model accuracy. We introduce a clustered data sharing framework, mitigating data heterogeneity by selectively sharing partial data from cluster heads to trusted associates. Experiments show that the proposed framework facilitates FEEL on non-IID datasets with faster convergence rate and higher model accuracy in a limited communication environment.
  arXiv  Detail & Related papers  (2024-06-14T07:22:39Z)
• SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead [75.87007729801304]
  SpaFL: a communication-efficient FL framework is proposed to optimize sparse model structures with low computational overhead. Experiments show that SpaFL improves accuracy while requiring much less communication and computing resources compared to sparse baselines.
  arXiv  Detail & Related papers  (2024-06-01T13:10:35Z)
• FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data [21.341280782748278]
  Clustered federated learning (CFL) is proposed to mitigate the performance deterioration stemming from data heterogeneity inFL. We propose an adaptive CFL framework, named FedAC, which efficiently integrates global knowledge into intra-cluster learning. Experiments show that FedAC achieves superior empirical performance, increasing the test accuracy by around 1.82% and 12.67%.
  arXiv  Detail & Related papers  (2024-03-25T06:43:28Z)
• FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing [14.938531944702193]
  We propose Federated Learning with Local Heterogeneous Sharing (FedLPS) FedLPS uses transfer learning to facilitate the deployment of multiple tasks on a single device by dividing the local model into a shareable encoder and task-specific encoders. FedLPS significantly outperforms the state-of-the-art (SOTA) FL frameworks by up to 4.88% and reduces the computational resource consumption by 21.3%.
  arXiv  Detail & Related papers  (2024-02-13T16:30:30Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design [18.675244280002428]
  We propose a novel FL framework named coded federated learning (SCFL) that leverages coded computing techniques. In SCFL, each edge device uploads a privacy-preserving coded dataset to the server, which is generated by adding noise to the projected local dataset. We show that SCFL learns a better model within the given time and achieves a better privacy-performance tradeoff than the baseline methods.
  arXiv  Detail & Related papers  (2022-11-08T09:58:36Z)
• FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity [4.2059108111562935]
  Federated Learning (FL) is an emerging framework for distributed processing of large data volumes by edge devices. In this paper, we introduce a new FLAD FedADMM based protocol. We show that FedADMM consistently outperforms all baseline methods in terms of communication efficiency.
  arXiv  Detail & Related papers  (2022-04-07T15:58:33Z)
• Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation [48.821062916381685]
  Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. In this work, we propose an efficient reinforcement learning(RL)-based federated hyperparameter optimization algorithm, termed Auto-FedRL. The effectiveness of the proposed method is validated on a heterogeneous data split of the CIFAR-10 dataset and two real-world medical image segmentation datasets.
  arXiv  Detail & Related papers  (2022-03-12T04:11:42Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)

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.