Guiding The Last Layer in Federated Learning with Pre-Trained Models

• URL: http://arxiv.org/abs/2306.03937v2
• Date: Mon, 6 Nov 2023 18:19:49 GMT
• Title: Guiding The Last Layer in Federated Learning with Pre-Trained Models
• Authors: Gwen Legate, Nicolas Bernier, Lucas Caccia, Edouard Oyallon, Eugene Belilovsky
• Abstract summary: Federated Learning (FL) is an emerging paradigm that allows a model to be trained across a number of participants without sharing data. We show that fitting a classification head using the Nearest Class Means (NCM) can be done exactly and orders of magnitude more efficiently than existing proposals.
• Score: 18.382057374270143
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) is an emerging paradigm that allows a model to be trained across a number of participants without sharing data. Recent works have begun to consider the effects of using pre-trained models as an initialization point for existing FL algorithms; however, these approaches ignore the vast body of efficient transfer learning literature from the centralized learning setting. Here we revisit the problem of FL from a pre-trained model considered in prior work and expand it to a set of computer vision transfer learning problems. We first observe that simply fitting a linear classification head can be efficient and effective in many cases. We then show that in the FL setting, fitting a classifier using the Nearest Class Means (NCM) can be done exactly and orders of magnitude more efficiently than existing proposals, while obtaining strong performance. Finally, we demonstrate that using a two-phase approach of obtaining the classifier and then fine-tuning the model can yield rapid convergence and improved generalization in the federated setting. We demonstrate the potential our method has to reduce communication and compute costs while achieving better model performance.

Related papers

• Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning [33.88701368538447]
  We propose an innovative model-based local training technique called Local Superior Soups'' Our method enhances local training across different clients, encouraging the exploration of a connected low-loss basin. We demonstrated its effectiveness and efficiency across diverse widely-used FL datasets.
  arXiv  Detail & Related papers  (2024-10-31T06:20:17Z)
• Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment [65.15914284008973]
  We propose to leverage an Inverse Reinforcement Learning (IRL) technique to simultaneously build an reward model and a policy model. We show that the proposed algorithms converge to the stationary solutions of the IRL problem. Our results indicate that it is beneficial to leverage reward learning throughout the entire alignment process.
  arXiv  Detail & Related papers  (2024-05-28T07:11:05Z)
• FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning [21.693779973263172]
  In this paper, we introduce a fine-tuning approach termed Feature Discrimination Alignment (FD-Align) Our method aims to bolster the model's generalizability by preserving the consistency of spurious features. Once fine-tuned, the model can seamlessly integrate with existing methods, leading to performance improvements.
  arXiv  Detail & Related papers  (2023-10-23T17:12:01Z)
• RanPAC: Random Projections and Pre-trained Models for Continual Learning [59.07316955610658]
  Continual learning (CL) aims to learn different tasks (such as classification) in a non-stationary data stream without forgetting old ones. We propose a concise and effective approach for CL with pre-trained models.
  arXiv  Detail & Related papers  (2023-07-05T12:49:02Z)
• Model Sparsity Can Simplify Machine Unlearning [33.18951938708467]
  In response to recent data regulation requirements, machine unlearning (MU) has emerged as a critical process. Our study introduces a novel model-based perspective: model sparsification via weight pruning. We show in both theory and practice that model sparsity can boost the multi-criteria unlearning performance of an approximate unlearner.
  arXiv  Detail & Related papers  (2023-04-11T02:12:02Z)
• CoopInit: Initializing Generative Adversarial Networks via Cooperative Learning [50.90384817689249]
  CoopInit is a cooperative learning-based strategy that can quickly learn a good starting point for GANs. We demonstrate the effectiveness of the proposed approach on image generation and one-sided unpaired image-to-image translation tasks.
  arXiv  Detail & Related papers  (2023-03-21T07:49:32Z)
• Faster Adaptive Federated Learning [84.38913517122619]
  Federated learning has attracted increasing attention with the emergence of distributed data. In this paper, we propose an efficient adaptive algorithm (i.e., FAFED) based on momentum-based variance reduced technique in cross-silo FL.
  arXiv  Detail & Related papers  (2022-12-02T05:07:50Z)
• Conquering the Communication Constraints to Enable Large Pre-Trained Models in Federated Learning [18.12162136918301]
  Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training of models without centralized access to the raw data on local devices. Recent state-of-the-art pre-trained models are getting more capable but also have more parameters. Can we find a solution to enable those strong and readily-available pre-trained models in FL to achieve excellent performance while simultaneously reducing the communication burden? Specifically, we systemically evaluate the performance of FedPEFT across a variety of client stability, data distribution, and differential privacy settings.
  arXiv  Detail & Related papers  (2022-10-04T16:08:54Z)
• Beyond Transfer Learning: Co-finetuning for Action Localisation [64.07196901012153]
  We propose co-finetuning -- simultaneously training a single model on multiple upstream'' and downstream'' tasks. We demonstrate that co-finetuning outperforms traditional transfer learning when using the same total amount of data. We also show how we can easily extend our approach to multiple upstream'' datasets to further improve performance.
  arXiv  Detail & Related papers  (2022-07-08T10:25:47Z)
• Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language Transfer Learning [59.38343286807997]
  We propose Model-Agnostic Multitask Fine-tuning (MAMF) for vision-language models on unseen tasks. Compared with model-agnostic meta-learning (MAML), MAMF discards the bi-level optimization and uses only first-order gradients. We show that MAMF consistently outperforms the classical fine-tuning method for few-shot transfer learning on five benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-09T17:26:53Z)

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.