FedSynth: Gradient Compression via Synthetic Data in Federated Learning

• URL: http://arxiv.org/abs/2204.01273v1
• Date: Mon, 4 Apr 2022 06:47:20 GMT
• Title: FedSynth: Gradient Compression via Synthetic Data in Federated Learning
• Authors: Shengyuan Hu, Jack Goetz, Kshitiz Malik, Hongyuan Zhan, Zhe Liu, Yue Liu
• Abstract summary: We propose a new scheme for upstream communication where instead of transmitting the model update, each client learns and transmits a light-weight synthetic dataset. We find our method is comparable/better than random masking baselines in all three common federated learning benchmark datasets.
• Score: 14.87215762562876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model compression is important in federated learning (FL) with large models to reduce communication cost. Prior works have been focusing on sparsification based compression that could desparately affect the global model accuracy. In this work, we propose a new scheme for upstream communication where instead of transmitting the model update, each client learns and transmits a light-weight synthetic dataset such that using it as the training data, the model performs similarly well on the real training data. The server will recover the local model update via the synthetic data and apply standard aggregation. We then provide a new algorithm FedSynth to learn the synthetic data locally. Empirically, we find our method is comparable/better than random masking baselines in all three common federated learning benchmark datasets.

Related papers

• Little Giants: Synthesizing High-Quality Embedding Data at Scale [71.352883755806]
  We introduce SPEED, a framework that aligns open-source small models to efficiently generate large-scale embedding data. SPEED uses only less than 1/10 of the GPT API calls, outperforming the state-of-the-art embedding model E5_mistral when both are trained solely on their synthetic data.
  arXiv  Detail & Related papers  (2024-10-24T10:47:30Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN [1.5749416770494706]
  Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices. We propose FLIGAN, a novel approach to address the issue of data incompleteness in FL. Our methodology adheres to FL's privacy requirements by generating synthetic data in a federated manner without sharing the actual data in the process.
  arXiv  Detail & Related papers  (2024-03-25T16:49:38Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• Fake It Till Make It: Federated Learning with Consensus-Oriented Generation [52.82176415223988]
  We propose federated learning with consensus-oriented generation (FedCOG) FedCOG consists of two key components at the client side: complementary data generation and knowledge-distillation-based model training. Experiments on classical and real-world FL datasets show that FedCOG consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-10T18:49:59Z)
• DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics [60.60173139258481]
  Local training on non-iid distributed data results in deflected local optimum. A natural solution is to gather all client data onto the server, such that the server has a global view of the entire data distribution. In this paper, we put forth an idea to collect and leverage global knowledge on the server without hindering data privacy.
  arXiv  Detail & Related papers  (2022-11-20T06:13:06Z)
• Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design [18.675244280002428]
  We propose a novel FL framework named coded federated learning (SCFL) that leverages coded computing techniques. In SCFL, each edge device uploads a privacy-preserving coded dataset to the server, which is generated by adding noise to the projected local dataset. We show that SCFL learns a better model within the given time and achieves a better privacy-performance tradeoff than the baseline methods.
  arXiv  Detail & Related papers  (2022-11-08T09:58:36Z)
• FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning [87.08902493524556]
  Federated learning(FL) has recently attracted increasing attention from academia and industry. We propose FedDM to build the global training objective from multiple local surrogate functions. In detail, we construct synthetic sets of data on each client to locally match the loss landscape from original data.
  arXiv  Detail & Related papers  (2022-07-20T04:55:18Z)
• Using GPT-2 to Create Synthetic Data to Improve the Prediction Performance of NLP Machine Learning Classification Models [0.0]
  It is becoming common practice to utilize synthetic data to boost the performance of Machine Learning Models. I used a Yelp pizza restaurant reviews dataset and transfer learning to fine-tune a pre-trained GPT-2 Transformer Model to generate synthetic pizza reviews data. I then combined this synthetic data with the original genuine data to create a new joint dataset.
  arXiv  Detail & Related papers  (2021-04-02T20:20:42Z)
• Coded Federated Learning [5.375775284252717]
  Federated learning is a method of training a global model from decentralized data distributed across client devices. Our results show that CFL allows the global model to converge nearly four times faster when compared to an uncoded approach.
  arXiv  Detail & Related papers  (2020-02-21T23:06:20Z)

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.