$\mathsf{OPA}$: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning

• URL: http://arxiv.org/abs/2410.22303v1
• Date: Tue, 29 Oct 2024 17:50:11 GMT
• Title: $\mathsf{OPA}$: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning
• Authors: Harish Karthikeyan, Antigoni Polychroniadou,
• Abstract summary: We introduce One-shot Private Aggregation ($mathsfOPA$) where clients speak only once (or even choose not to speak) per aggregation evaluation. Since each client communicates only once per aggregation, this simplifies managing dropouts and dynamic participation. $mathsfOPA$ is practical, outperforming state-of-the-art solutions.
• Score: 6.977111770337479
• License:
• Abstract: Our work aims to minimize interaction in secure computation due to the high cost and challenges associated with communication rounds, particularly in scenarios with many clients. In this work, we revisit the problem of secure aggregation in the single-server setting where a single evaluation server can securely aggregate client-held individual inputs. Our key contribution is the introduction of One-shot Private Aggregation ($\mathsf{OPA}$) where clients speak only once (or even choose not to speak) per aggregation evaluation. Since each client communicates only once per aggregation, this simplifies managing dropouts and dynamic participation, contrasting with multi-round protocols and aligning with plaintext secure aggregation, where clients interact only once. We construct $\mathsf{OPA}$ based on LWR, LWE, class groups, DCR and demonstrate applications to privacy-preserving Federated Learning (FL) where clients \emph{speak once}. This is a sharp departure from prior multi-round FL protocols whose study was initiated by Bonawitz et al. (CCS, 2017). Moreover, unlike the YOSO (You Only Speak Once) model for general secure computation, $\mathsf{OPA}$ eliminates complex committee selection protocols to achieve adaptive security. Beyond asymptotic improvements, $\mathsf{OPA}$ is practical, outperforming state-of-the-art solutions. We benchmark logistic regression classifiers for two datasets, while also building an MLP classifier to train on MNIST, CIFAR-10, and CIFAR-100 datasets. We build two flavors of $\caps$ (1) from (threshold) key homomorphic PRF and (2) from seed homomorphic PRG and secret sharing.

Related papers

• Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis [14.98493572536424]
  In federated learning, it is common to assume that clients are always available to participate in training, which may not be feasible with user devices in practice. Recent analyze federated learning under more realistic participation patterns as cyclic client availability or arbitrary participation.
  arXiv  Detail & Related papers  (2024-10-30T15:41:35Z)
• Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning [51.560590617691005]
  We investigate whether it is possible to squeeze more juice" out of each cohort than what is possible in a single communication round. Our approach leads to up to 74% reduction in the total communication cost needed to train a FL model in the cross-device setting.
  arXiv  Detail & Related papers  (2024-06-03T08:48:49Z)
• Federated Combinatorial Multi-Agent Multi-Armed Bandits [79.1700188160944]
  This paper introduces a federated learning framework tailored for online optimization with bandit. In this setting, agents subsets of arms, observe noisy rewards for these subsets without accessing individual arm information, and can cooperate and share information at specific intervals.
  arXiv  Detail & Related papers  (2024-05-09T17:40:09Z)
• Learn What You Need in Personalized Federated Learning [53.83081622573734]
  $textitLearn2pFed$ is a novel algorithm-unrolling-based personalized federated learning framework. We show that $textitLearn2pFed$ significantly outperforms previous personalized federated learning methods.
  arXiv  Detail & Related papers  (2024-01-16T12:45:15Z)
• 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)
• Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications [26.597515045714502]
  Federated learning (FL) is a decentralized learning framework wherein a parameter server (PS) and a collection of clients collaboratively train a model via a global objective. We show that when the channel conditions are heterogeneous across clients are changing over time, the FedFederated Postponed global model fails to postpone the gossip-type information mixing errors.
  arXiv  Detail & Related papers  (2023-06-01T01:52:03Z)
• 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)
• Federated Learning with Regularized Client Participation [1.433758865948252]
  Federated Learning (FL) is a distributed machine learning approach where multiple clients work together to solve a machine learning task. One of the key challenges in FL is the issue of partial participation, which occurs when a large number of clients are involved in the training process. We propose a new technique and design a novel regularized client participation scheme.
  arXiv  Detail & Related papers  (2023-02-07T18:26:07Z)
• 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)
• Federated Stochastic Primal-dual Learning with Differential Privacy [15.310299472656533]
  We propose a new federated primal-dual algorithm with differential privacy (FedSPDDP) Our analysis shows that the data sampling strategy and PCP can enhance the data privacy whereas the larger number of local SGD steps could increase privacy leakage. Experiment results are presented to evaluate the practical performance of the proposed algorithm.
  arXiv  Detail & Related papers  (2022-04-26T13:10:37Z)
• Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees [43.10380224532313]
  We study the emergence of coordinated behavior by autonomous agents using an actor-critic (AC) algorithm. We propose and analyze a class of coordinated actor-critic algorithms (CAC) in which individually parametrized policies have a it shared part and a it personalized part. This work provides the first finite-sample guarantee for decentralized AC algorithm with partially personalized policies.
  arXiv  Detail & Related papers  (2021-10-11T20:26:16Z)

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.