FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation

• URL: http://arxiv.org/abs/2505.23182v2
• Date: Tue, 17 Jun 2025 00:36:43 GMT
• Title: FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation
• Authors: Srijith Nair, Michael Lin, Peizhong Ju, Amirreza Talebi, Elizabeth Serena Bentley, Jia Liu,
• Abstract summary: Collaborative training methods like Federated Learning (FL) and Split Learning (SL) enable distributed machine learning without sharing raw data.<n>We propose FSL-SAGE, a new federated split learning algorithm that estimates server-side gradient feedback via auxiliary models.
• Score: 13.034619943612311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative training methods like Federated Learning (FL) and Split Learning (SL) enable distributed machine learning without sharing raw data. However, FL assumes clients can train entire models, which is infeasible for large-scale models. In contrast, while SL alleviates the client memory constraint in FL by offloading most training to the server, it increases network latency due to its sequential nature. Other methods address the conundrum by using local loss functions for parallel client-side training to improve efficiency, but they lack server feedback and potentially suffer poor accuracy. We propose FSL-SAGE (Federated Split Learning via Smashed Activation Gradient Estimation), a new federated split learning algorithm that estimates server-side gradient feedback via auxiliary models. These auxiliary models periodically adapt to emulate server behavior on local datasets. We show that FSL-SAGE achieves a convergence rate of $\mathcal{O}(1/\sqrt{T})$, where $T$ is the number of communication rounds. This result matches FedAvg, while significantly reducing communication costs and client memory requirements. Our empirical results also verify that it outperforms existing state-of-the-art FSL methods, offering both communication efficiency and accuracy.

Related papers

• Federated Split Learning with Improved Communication and Storage Efficiency [9.277834710955766]
  Federated learning (FL) is one of the popular distributed machine learning (ML) solutions but incurs significant communication and computation costs at edge devices.<n>This paper proposes a novel communication and storage efficient federated split learning method, CSE-FSL, which utilizes an auxiliary network to locally update the clients while keeping a single model at the server.
  arXiv  Detail & Related papers  (2025-07-21T17:21:16Z)
• CELLM: An Efficient Communication in Large Language Models Training for Federated Learning [0.0]
  This thesis aims to develop efficient training methods for large language models (LLMs) in Federated Learning (FL) First, we use low-rank adaptation (LoRA) to reduce the computational load of local model training. Second, we communicate sparse updates throughout training to significantly cut down on communication costs.
  arXiv  Detail & Related papers  (2024-07-30T05:24:08Z)
• Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learning [56.21666819468249]
  In Federated Learning (FL), both client resource constraints and communication costs pose major problems for training large models.<n>Recent work has shown that local training provably improves communication complexity through acceleration.<n>We introduce Sparse-ProxSkip, addressing the issue and implementing the efficient technique of Straight-Through Estorimat pruning into sparse training.
  arXiv  Detail & Related papers  (2024-05-31T05:21:12Z)
• 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)
• LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning [14.937324556028548]
  We propose the Layer-Wise Federated Self-Supervised Learning approach, which allows edge devices to incrementally train a small part of the model at a time.<n>Since only a portion of the model is active for training at any given time, LW-FedSSL significantly reduces computational requirements.<n>It can achieve up to a $3.34 times$ reduction in memory usage, $4.20 times$ fewer computational operations (GFLOPs), and a $5.07 times$ lower communication cost.
  arXiv  Detail & Related papers  (2024-01-22T01:57:31Z)
• 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)
• Subspace based Federated Unlearning [75.90552823500633]
  Federated unlearning (FL) aims to remove a specified target client's contribution in FL to satisfy the user's right to be forgotten. Most existing federated unlearning algorithms require the server to store the history of the parameter updates. We propose a simple-yet-effective subspace based federated unlearning method, dubbed SFU, that lets the global model perform gradient ascent.
  arXiv  Detail & Related papers  (2023-02-24T04:29:44Z)
• Communication and Storage Efficient Federated Split Learning [19.369076939064904]
  Federated Split Learning preserves the parallel model training principle of FL. Server has to maintain separate models for every client, resulting in a significant computation and storage requirement. This paper proposes a communication and storage efficient federated and split learning strategy.
  arXiv  Detail & Related papers  (2023-02-11T04:44:29Z)
• Scalable Collaborative Learning via Representation Sharing [53.047460465980144]
  Federated learning (FL) and Split Learning (SL) are two frameworks that enable collaborative learning while keeping the data private (on device) In FL, each data holder trains a model locally and releases it to a central server for aggregation. In SL, the clients must release individual cut-layer activations (smashed data) to the server and wait for its response (during both inference and back propagation). In this work, we present a novel approach for privacy-preserving machine learning, where the clients collaborate via online knowledge distillation using a contrastive loss.
  arXiv  Detail & Related papers  (2022-11-20T10:49:22Z)
• Federated Learning with Server Learning: Enhancing Performance for Non-IID Data [5.070289965695956]
  Federated Learning (FL) has emerged as a means of distributed learning using local data stored at clients with a coordinating server. Recent studies showed that FL can suffer from poor performance and slower convergence when training data at clients are not independent and identically distributed. Here we consider a new complementary approach to mitigating this performance degradation by allowing the server to perform auxiliary learning from a small dataset.
  arXiv  Detail & Related papers  (2022-10-06T00:27:16Z)
• 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)
• Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning [82.06357027523262]
  Federated learning (FL) and split learning (SL) are two spearheads possessing their pros and cons, and are suited for many user clients and large models. In this work, we first identify the fundamental bottlenecks of SL, and thereby propose a scalable SL framework, coined SGLR.
  arXiv  Detail & Related papers  (2021-12-11T08:33:25Z)
• SplitFed: When Federated Learning Meets Split Learning [16.212941272007285]
  Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. This paper presents a novel approach, named splitfed learning (SFL), that amalgamates the two approaches. SFL provides similar test accuracy and communication efficiency as SL while significantly decreasing its computation time per global epoch than in SL for multiple clients.
  arXiv  Detail & Related papers  (2020-04-25T08:52:50Z)

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.