Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching

• URL: http://arxiv.org/abs/2109.08346v2
• Date: Sat, 30 Sep 2023 07:12:33 GMT
• Title: Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching
• Authors: Tahseen Rabbani, Brandon Feng, Marco Bornstein, Kyle Rui Sang, Yifan Yang, Arjun Rajkumar, Amitabh Varshney, Furong Huang
• Abstract summary: Federated learning (FL) is a popular paradigm for private and collaborative model training on the edge. We propose a novel algorithm, Comdirectional, which allows clients to train large networks using representations of the global neural network.
• Score: 28.990067638230254
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning (FL) is a popular paradigm for private and collaborative model training on the edge. In centralized FL, the parameters of a global architecture (such as a deep neural network) are maintained and distributed by a central server/controller to clients who transmit model updates (gradients) back to the server based on local optimization. While many efforts have focused on reducing the communication complexity of gradient transmission, the vast majority of compression-based algorithms assume that each participating client is able to download and train the current and full set of parameters, which may not be a practical assumption depending on the resource constraints of smaller clients such as mobile devices. In this work, we propose a simple yet effective novel algorithm, Comfetch, which allows clients to train large networks using reduced representations of the global architecture via the count sketch, which reduces local computational and memory costs along with bi-directional communication complexity. We provide a nonconvex convergence guarantee and experimentally demonstrate that it is possible to learn large models, such as a deep convolutional network, through federated training on their sketched counterparts. The resulting global models exhibit competitive test accuracy over CIFAR10/100 classification when compared against un-compressed model training.

Related papers

• Efficient Model Compression for Hierarchical Federated Learning [10.37403547348343]
  Federated learning (FL) has garnered significant attention due to its capacity to preserve privacy within distributed learning systems. This paper introduces a novel hierarchical FL framework that integrates the benefits of clustered FL and model compression.
  arXiv  Detail & Related papers  (2024-05-27T12:17:47Z)
• Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach [67.27031215756121]
  Federated learning (FL) is a machine learning paradigm that targets model training without gathering the local data over various data sources. Standard FL, which employs a single server, can only support a limited number of users, leading to degraded learning capability. In this work, we consider a multi-server FL framework, referred to as emphConfederated Learning (CFL) in order to accommodate a larger number of users.
  arXiv  Detail & Related papers  (2024-02-28T03:27:10Z)
• 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)
• Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms [29.636944156801327]
  Multiple clients collaboratively train one global model without sharing their semantic parsing data. Lorar adjusts each client's contribution to the global model update based on its training loss reduction during each round. Clients with smaller datasets enjoy larger performance gains.
  arXiv  Detail & Related papers  (2023-05-26T19:25:49Z)
• 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)
• ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals [24.13593410107805]
  Federated learning enables cooperative training among massively distributed clients by sharing their learned local model parameters. We introduce a residual-based federated learning framework (ResFed), where residuals rather than model parameters are transmitted in communication networks for training. By employing a common prediction rule, both locally and globally updated models are always fully recoverable in clients and the server.
  arXiv  Detail & Related papers  (2022-12-11T20:34:52Z)
• DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training [84.81043932706375]
  We propose a novel personalized federated learning framework in a decentralized (peer-to-peer) communication protocol named Dis-PFL. Dis-PFL employs personalized sparse masks to customize sparse local models on the edge. We demonstrate that our method can easily adapt to heterogeneous local clients with varying computation complexities.
  arXiv  Detail & Related papers  (2022-06-01T02:20:57Z)
• 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)
• Architecture Agnostic Federated Learning for Neural Networks [19.813602191888837]
  This work introduces a novel Federated Heterogeneous Neural Networks (FedHeNN) framework. FedHeNN allows each client to build a personalised model without enforcing a common architecture across clients. The key idea of FedHeNN is to use the instance-level representations obtained from peer clients to guide the simultaneous training on each client.
  arXiv  Detail & Related papers  (2022-02-15T22:16:06Z)
• A Bayesian Federated Learning Framework with Online Laplace Approximation [144.7345013348257]
  Federated learning allows multiple clients to collaboratively learn a globally shared model. We propose a novel FL framework that uses online Laplace approximation to approximate posteriors on both the client and server side. We achieve state-of-the-art results on several benchmarks, clearly demonstrating the advantages of the proposed method.
  arXiv  Detail & Related papers  (2021-02-03T08:36:58Z)
• Coded Federated Learning [5.375775284252717]
  Federated learning is a method of training a global model from decentralized data distributed across client devices. Our results show that CFL allows the global model to converge nearly four times faster when compared to an uncoded approach.
  arXiv  Detail & Related papers  (2020-02-21T23:06:20Z)

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.