FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization

• URL: http://arxiv.org/abs/2404.13575v1
• Date: Sun, 21 Apr 2024 08:27:36 GMT
• Title: FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization
• Authors: Xu Yang, Jiapeng Zhang, Qifeng Zhang, Zhuo Tang,
• Abstract summary: We propose a novel uplink communication compression method for federated learning, named FedMPQ. In contrast to previous works, our approach exhibits greater robustness in scenarios where data is not independently and identically distributed. Experiments conducted on the LEAF dataset demonstrate that our proposed method achieves 99% of the baseline's final accuracy.
• Score: 12.83265009728818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In federated learning, particularly in cross-device scenarios, secure aggregation has recently gained popularity as it effectively defends against inference attacks by malicious aggregators. However, secure aggregation often requires additional communication overhead and can impede the convergence rate of the global model, which is particularly challenging in wireless network environments with extremely limited bandwidth. Therefore, achieving efficient communication compression under the premise of secure aggregation presents a highly challenging and valuable problem. In this work, we propose a novel uplink communication compression method for federated learning, named FedMPQ, which is based on multi shared codebook product quantization.Specifically, we utilize updates from the previous round to generate sufficiently robust codebooks. Secure aggregation is then achieved through trusted execution environments (TEE) or a trusted third party (TTP).In contrast to previous works, our approach exhibits greater robustness in scenarios where data is not independently and identically distributed (non-IID) and there is a lack of sufficient public data. The experiments conducted on the LEAF dataset demonstrate that our proposed method achieves 99% of the baseline's final accuracy, while reducing uplink communications by 90-95%

Related papers

• Secure Stateful Aggregation: A Practical Protocol with Applications in Differentially-Private Federated Learning [36.42916779389165]
  DP-FTRL based approaches have already seen widespread deployment in industry. We introduce secure stateful aggregation: a simple append-only data structure that allows for the private storage of aggregate values. We observe that secure stateful aggregation suffices for realizing DP-FTRL-based private federated learning.
  arXiv  Detail & Related papers  (2024-10-15T07:45:18Z)
• PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning [49.916365792036636]
  Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data. The transmitted model updates can potentially leak sensitive user information, and the lack of central control of the local training process leaves the global model susceptible to malicious manipulations on model updates. We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy.
  arXiv  Detail & Related papers  (2024-07-12T03:18:08Z)
• 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)
• Coding-Based Hybrid Post-Quantum Cryptosystem for Non-Uniform Information [53.85237314348328]
  We introduce for non-uniform messages a novel hybrid universal network coding cryptosystem (NU-HUNCC) We show that NU-HUNCC is information-theoretic individually secured against an eavesdropper with access to any subset of the links.
  arXiv  Detail & Related papers  (2024-02-13T12:12:39Z)
• 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)
• FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification [65.7405397206767]
  We propose Federated Deep-Broad Learning (FedDBL) to achieve superior classification performance with limited training samples and only one-round communication. FedDBL greatly outperforms the competitors with only one-round communication and limited training samples, while it even achieves comparable performance with the ones under multiple-round communications. Since no data or deep model sharing across different clients, the privacy issue is well-solved and the model security is guaranteed with no model inversion attack risk.
  arXiv  Detail & Related papers  (2023-02-24T14:27:41Z)
• Reconciling Security and Communication Efficiency in Federated Learning [11.653872280157321]
  Cross-device Federated Learning is an increasingly popular machine learning setting. In this paper, we formalize and address the problem of compressing client-to-server model updates. We establish state-of-the-art results on LEAF benchmarks in a secure Federated Learning setup with up to 40$times$ compression in uplink communication.
  arXiv  Detail & Related papers  (2022-07-26T09:52:55Z)
• Sparsified Secure Aggregation for Privacy-Preserving Federated Learning [1.2891210250935146]
  We propose a lightweight gradient sparsification framework for secure aggregation. Our theoretical analysis demonstrates that the proposed framework can significantly reduce the communication overhead of secure aggregation. Our experiments demonstrate that our framework reduces the communication overhead by up to 7.8x, while also speeding up the wall clock training time by 1.13x, when compared to conventional secure aggregation benchmarks.
  arXiv  Detail & Related papers  (2021-12-23T22:44:21Z)
• Learning, compression, and leakage: Minimising classification error via meta-universal compression principles [87.054014983402]
  A promising group of compression techniques for learning scenarios is normalised maximum likelihood (NML) coding. Here we consider a NML-based decision strategy for supervised classification problems, and show that it attains PAC learning when applied to a wide variety of models. We show that the misclassification rate of our method is upper bounded by the maximal leakage, a recently proposed metric to quantify the potential of data leakage in privacy-sensitive scenarios.
  arXiv  Detail & Related papers  (2020-10-14T20:03:58Z)
• WAFFLe: Weight Anonymized Factorization for Federated Learning [88.44939168851721]
  In domains where data are sensitive or private, there is great value in methods that can learn in a distributed manner without the data ever leaving the local devices. We propose Weight Anonymized Factorization for Federated Learning (WAFFLe), an approach that combines the Indian Buffet Process with a shared dictionary of weight factors for neural networks.
  arXiv  Detail & Related papers  (2020-08-13T04:26:31Z)
• Ternary Compression for Communication-Efficient Federated Learning [17.97683428517896]
  Federated learning provides a potential solution to privacy-preserving and secure machine learning. We propose a ternary federated averaging protocol (T-FedAvg) to reduce the upstream and downstream communication of federated learning systems. Our results show that the proposed T-FedAvg is effective in reducing communication costs and can even achieve slightly better performance on non-IID data.
  arXiv  Detail & Related papers  (2020-03-07T11:55:34Z)

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.