Client-Cooperative Split Learning

• URL: http://arxiv.org/abs/2603.08421v1
• Date: Mon, 09 Mar 2026 14:17:26 GMT
• Title: Client-Cooperative Split Learning
• Abstract summary: Split Learning (SL) enables services by offloading training computation under privacy constraints.<n>We present CliCooper, a multi-client cooperative SL framework tailored for cooperative model training services.<n>CliCooper bridges the privacy and trust gaps through two new designs.
• Score: 22.47753142358317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model training is increasingly offered as a service for resource-constrained data owners to build customized models. Split Learning (SL) enables such services by offloading training computation under privacy constraints, and evolves toward serverless and multi-client settings where model segments are distributed across training clients. This cooperative mode assumes partial trust: data owners hide labels and data from trainer clients, while trainer clients produce verifiable training artifacts and ownership proofs. We present CliCooper, a multi-client cooperative SL framework tailored for cooperative model training services in heterogeneous and partially trusted environments, where one client contributes data, while others collectively act as SL trainers. CliCooper bridges the privacy and trust gaps through two new designs. First, differential privacy-based activation protection and secret label obfuscation safeguard data owners' privacy without degrading model performance. Second, a dynamic chained watermarking scheme cryptographically links training stages on model segments across trainers, ensuring verifiable training integrity, robust model provenance, and copyright protection. Experiments show that CliCooper preserves model accuracy while enhancing resilience to privacy and ownership attacks. It reduces the success rate of clustering attacks (which infer label groups from intermediate activation) to 0%, decreases inversion-reconstruction (which recovers training data) similarity from 0.50 to 0.03, and limits model-extraction-based surrogates to about 1% accuracy, comparable to random guessing.

Related papers

• Decentralized Privacy-Preserving Federal Learning of Computer Vision Models on Edge Devices [0.0]
  Collaborative training of a machine learning model comes with a risk of sharing sensitive or private data.<n> Federated learning offers a way of collectively training a single global model without the need to share client data.<n>We have analyzed the negative effects of gradient compression and gradient noising on the accuracy of convolutional neural networks used for classification.
  arXiv  Detail & Related papers  (2026-01-08T13:10:33Z)
• Enhancing Federated Learning Privacy with QUBO [0.0]
  Federated learning (FL) is a widely used method for training machine learning (ML) models in a scalable way while preserving privacy.<n>In this paper, we focus on two threat vectors: information leakage by clients during training and adversaries who can query or obtain the global model.<n> Experiments on the MNIST dataset with 300 clients in 20 rounds showed a 95.2% per-round and 49% cumulative privacy exposure reduction.
  arXiv  Detail & Related papers  (2025-11-04T18:06:30Z)
• ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version) [58.595691399741646]
  Split Learning (SL) is a distributed learning approach that enables resource-constrained clients to collaboratively train deep neural networks (DNNs)<n>This setup enables SL to leverage server capacities without sharing data, making it highly effective in resource-constrained environments dealing with sensitive data.<n>We present ZORRO, a private, verifiable, and robust SL defense scheme.
  arXiv  Detail & Related papers  (2025-09-11T18:44:09Z)
• A Vision-Language Pre-training Model-Guided Approach for Mitigating Backdoor Attacks in Federated Learning [43.847168319564844]
  We propose an FL backdoor defense framework, named CLIP-Fed, that utilizes the zero-shot learning capabilities of vision-language pre-training models.<n>Our scheme overcomes the limitations of Non-IID imposed on defense effectiveness by integrating pre-aggregation and post-aggregation defense strategies.
  arXiv  Detail & Related papers  (2025-08-14T03:39:54Z)
• Pigeon-SL: Robust Split Learning Framework for Edge Intelligence under Malicious Clients [53.496957000114875]
  We introduce Pigeon-SL, a novel scheme that guarantees at least one entirely honest cluster among M clients, even when up to N of them are adversarial.<n>In each global round, the access point partitions the clients into N+1 clusters, trains each cluster independently via vanilla SL, and evaluates their validation losses on a shared dataset.<n>Only the cluster with the lowest loss advances, thereby isolating and discarding malicious updates.
  arXiv  Detail & Related papers  (2025-08-04T09:34:50Z)
• SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version) [53.16528046390881]
  Split Learning (SL) is a distributed deep learning approach enabling multiple clients and a server to collaboratively train and infer on a shared deep neural network (DNN)<n>This paper presents SafeSplit, the first defense against client-side backdoor attacks in Split Learning (SL)<n>It uses a two-fold analysis to identify client-induced changes and detect poisoned models.
  arXiv  Detail & Related papers  (2025-01-11T22:20:20Z)
• Client-specific Property Inference against Secure Aggregation in Federated Learning [52.8564467292226]
  Federated learning has become a widely used paradigm for collaboratively training a common model among different participants. Many attacks have shown that it is still possible to infer sensitive information such as membership, property, or outright reconstruction of participant data. We show that simple linear models can effectively capture client-specific properties only from the aggregated model updates.
  arXiv  Detail & Related papers  (2023-03-07T14:11:01Z)
• 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)
• CrowdGuard: Federated Backdoor Detection in Federated Learning [39.58317527488534]
  This paper presents a novel defense mechanism, CrowdGuard, that effectively mitigates backdoor attacks in Federated Learning. CrowdGuard employs a server-located stacked clustering scheme to enhance its resilience to rogue client feedback. The evaluation results demonstrate that CrowdGuard achieves a 100% True-Positive-Rate and True-Negative-Rate across various scenarios.
  arXiv  Detail & Related papers  (2022-10-14T11:27:49Z)
• A Privacy-Preserving and Trustable Multi-agent Learning Framework [34.28936739262812]
  This paper presents Privacy-preserving and trustable Distributed Learning (PT-DL) PT-DL is a fully decentralized framework that relies on Differential Privacy to guarantee strong privacy protections of the agents' data. The paper shows that PT-DL is resilient up to a 50% collusion attack, with high probability, in a malicious trust model.
  arXiv  Detail & Related papers  (2021-06-02T15:46:27Z)
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation [119.19061102064497]
  We propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL) In a round of the proposed BLADE-FL, each client broadcasts its trained model to other clients, competes to generate a block based on the received models, and then aggregates the models from the generated block before its local training of the next round. We explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.
  arXiv  Detail & Related papers  (2021-01-18T07:19:08Z)

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.