Harnessing Sparsification in Federated Learning: A Secure, Efficient, and Differentially Private Realization

• URL: http://arxiv.org/abs/2511.07123v1
• Date: Mon, 10 Nov 2025 14:10:48 GMT
• Title: Harnessing Sparsification in Federated Learning: A Secure, Efficient, and Differentially Private Realization
• Authors: Shuangqing Xu, Yifeng Zheng, Zhongyun Hua,
• Abstract summary: Federated learning (FL) enables multiple clients to jointly train a model by sharing only gradient updates for aggregation instead of raw data.<n>We present Clover, a novel system framework for communication-efficient, secure, and differentially private FL.
• Score: 28.546805212017926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) enables multiple clients to jointly train a model by sharing only gradient updates for aggregation instead of raw data. Due to the transmission of very high-dimensional gradient updates from many clients, FL is known to suffer from a communication bottleneck. Meanwhile, the gradients shared by clients as well as the trained model may also be exploited for inferring private local datasets, making privacy still a critical concern in FL. We present Clover, a novel system framework for communication-efficient, secure, and differentially private FL. To tackle the communication bottleneck in FL, Clover follows a standard and commonly used approach-top-k gradient sparsification, where each client sparsifies its gradient update such that only k largest gradients (measured by magnitude) are preserved for aggregation. Clover provides a tailored mechanism built out of a trending distributed trust setting involving three servers, which allows to efficiently aggregate multiple sparse vectors (top-k sparsified gradient updates) into a dense vector while hiding the values and indices of non-zero elements in each sparse vector. This mechanism outperforms a baseline built on the general distributed ORAM technique by several orders of magnitude in server-side communication and runtime, with also smaller client communication cost. We further integrate this mechanism with a lightweight distributed noise generation mechanism to offer differential privacy (DP) guarantees on the trained model. To harden Clover with security against a malicious server, we devise a series of lightweight mechanisms for integrity checks on the server-side computation. Extensive experiments show that Clover can achieve utility comparable to vanilla FL with central DP, with promising performance.

Related papers

• FuSeFL: Fully Secure and Scalable Cross-Silo Federated Learning [0.696125353550498]
  Federated Learning (FL) enables collaborative model training without centralizing client data, making it attractive for privacy-sensitive domains.<n>We present FuSeFL, a fully secure and scalable FL scheme designed for cross-silo settings.
  arXiv  Detail & Related papers  (2025-07-18T00:50:44Z)
• BlindFL: Segmented Federated Learning with Fully Homomorphic Encryption [0.0]
  Federated learning (FL) is a privacy-preserving edge-to-cloud technique used for training and deploying AI models on edge devices.<n>BlindFL is a framework for global model aggregation in which clients encrypt and send a subset of their local model update.<n>BlindFL significantly impedes client-side model poisoning attacks, a first for single-key, FHE-based FL schemes.
  arXiv  Detail & Related papers  (2025-01-20T18:42:21Z)
• DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing [51.336015600778396]
  We introduce the distributed matrix mechanism to achieve the best-of-both-worlds; better privacy of distributed DP and better utility from the matrix mechanism.<n>We accomplish this using a novel cryptographic protocol that securely transfers sensitive values across client committees of different training iterations with constant communication overhead.
  arXiv  Detail & Related papers  (2024-10-21T16:25:14Z)
• Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy [9.100955087185811]
  Federated learning (FL) has rapidly become a compelling paradigm that enables multiple clients to jointly train a model by sharing only gradient updates for aggregation. In order to protect the gradient updates which could also be privacy-sensitive, there has been a line of work studying local differential privacy mechanisms. We present Camel, a new communication-efficient and maliciously secure FL framework in the shuffle model of DP.
  arXiv  Detail & Related papers  (2024-10-04T13:13:44Z)
• EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters [3.9660142560142067]
  Federated Learning (FL) enables model training across decentralized devices by communicating solely local model updates to an aggregation server. FL remains vulnerable to inference attacks during model update transmissions. We present EncCluster, a novel method that integrates model compression through weight clustering with recent decentralized FE and privacy-enhancing data encoding.
  arXiv  Detail & Related papers  (2024-06-13T14:16:50Z)
• Boosting Communication Efficiency of Federated Learning's Secure Aggregation [22.943966056320424]
  Federated Learning (FL) is a decentralized machine learning approach where client devices train models locally and send them to a server. FL is vulnerable to model inversion attacks, where the server can infer sensitive client data from trained models. Google's Secure Aggregation (SecAgg) protocol addresses this data privacy issue by masking each client's trained model. This poster introduces a Communication-Efficient Secure Aggregation (CESA) protocol that substantially reduces this overhead.
  arXiv  Detail & Related papers  (2024-05-02T10:00:16Z)
• 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)
• Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes [54.18186259484828]
  In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds. We show strong evidences that variable-length is beneficial for compression in FL. We present Fed-CVLC (Federated Learning Compression with Variable-Length Codes), which fine-tunes the code length in response to the dynamics of model updates.
  arXiv  Detail & Related papers  (2024-02-06T07:25:21Z)
• Federated Nearest Neighbor Machine Translation [66.8765098651988]
  In this paper, we propose a novel federated nearest neighbor (FedNN) machine translation framework. FedNN leverages one-round memorization-based interaction to share knowledge across different clients. Experiments show that FedNN significantly reduces computational and communication costs compared with FedAvg.
  arXiv  Detail & Related papers  (2023-02-23T18:04:07Z)
• 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)
• ScionFL: Efficient and Robust Secure Quantized Aggregation [36.668162197302365]
  We introduce ScionFL, the first secure aggregation framework for federated learning. It operates efficiently on quantized inputs and simultaneously provides robustness against malicious clients. We show that with no overhead for clients and moderate overhead for the server, we obtain comparable accuracy for standard FL benchmarks.
  arXiv  Detail & Related papers  (2022-10-13T21:46:55Z)
• 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)
• Over-the-Air Federated Learning from Heterogeneous Data [107.05618009955094]
  Federated learning (FL) is a framework for distributed learning of centralized models. We develop a Convergent OTA FL (COTAF) algorithm which enhances the common local gradient descent (SGD) FL algorithm. We numerically show that the precoding induced by COTAF notably improves the convergence rate and the accuracy of models trained via OTA FL.
  arXiv  Detail & Related papers  (2020-09-27T08:28:25Z)

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.