Federated Bayesian Optimization via Thompson Sampling

• URL: http://arxiv.org/abs/2010.10154v3
• Date: Thu, 22 Oct 2020 15:07:17 GMT
• Title: Federated Bayesian Optimization via Thompson Sampling
• Authors: Zhongxiang Dai, Kian Hsiang Low and Patrick Jaillet
• Abstract summary: This paper presents federated Thompson sampling (FTS) which overcomes a number of key challenges of FBO and FL in a principled way. We empirically demonstrate the effectiveness of FTS in terms of communication efficiency, computational efficiency, and practical performance.
• Score: 33.087439644066876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian optimization (BO) is a prominent approach to optimizing expensive-to-evaluate black-box functions. The massive computational capability of edge devices such as mobile phones, coupled with privacy concerns, has led to a surging interest in federated learning (FL) which focuses on collaborative training of deep neural networks (DNNs) via first-order optimization techniques. However, some common machine learning tasks such as hyperparameter tuning of DNNs lack access to gradients and thus require zeroth-order/black-box optimization. This hints at the possibility of extending BO to the FL setting (FBO) for agents to collaborate in these black-box optimization tasks. This paper presents federated Thompson sampling (FTS) which overcomes a number of key challenges of FBO and FL in a principled way: We (a) use random Fourier features to approximate the Gaussian process surrogate model used in BO, which naturally produces the parameters to be exchanged between agents, (b) design FTS based on Thompson sampling, which significantly reduces the number of parameters to be exchanged, and (c) provide a theoretical convergence guarantee that is robust against heterogeneous agents, which is a major challenge in FL and FBO. We empirically demonstrate the effectiveness of FTS in terms of communication efficiency, computational efficiency, and practical performance.

Related papers

• Bayesian Optimization for Hyperparameters Tuning in Neural Networks [0.0]
  Bayesian Optimization is a derivative-free global optimization method suitable for black-box functions with continuous inputs and limited evaluation budgets. This study investigates the application of BO for the hyper parameter tuning of neural networks, specifically targeting the enhancement of Convolutional Neural Networks (CNN) Experimental outcomes reveal that BO effectively balances exploration and exploitation, converging rapidly towards optimal settings for CNN architectures. This approach underlines the potential of BO in automating neural network tuning, contributing to improved accuracy and computational efficiency in machine learning pipelines.
  arXiv  Detail & Related papers  (2024-10-29T09:23:24Z)
• In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization [35.74766507227412]
  We propose FT-PFN, a novel surrogate for Freeze-thaw style BO. FT-PFN is a prior-data fitted network (PFN) that leverages the transformers' in-context learning ability. We show that, when combined with our novel acquisition mechanism (I-random), the resulting in-context freeze-thaw BO method (ifBO) yields new state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-04-25T17:40:52Z)
• Poisson Process for Bayesian Optimization [126.51200593377739]
  We propose a ranking-based surrogate model based on the Poisson process and introduce an efficient BO framework, namely Poisson Process Bayesian Optimization (PoPBO) Compared to the classic GP-BO method, our PoPBO has lower costs and better robustness to noise, which is verified by abundant experiments.
  arXiv  Detail & Related papers  (2024-02-05T02:54:50Z)
• Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization [102.92240148504774]
  We study a principled finetuning paradigm -- Orthogonal Finetuning (OFT) -- for downstream task adaptation. Despite demonstrating good generalizability, OFT still uses a fairly large number of trainable parameters. We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT)
  arXiv  Detail & Related papers  (2023-11-10T18:59:54Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• Sample-Then-Optimize Batch Neural Thompson Sampling [50.800944138278474]
  We introduce two algorithms for black-box optimization based on the Thompson sampling (TS) policy. To choose an input query, we only need to train an NN and then choose the query by maximizing the trained NN. Our algorithms sidestep the need to invert the large parameter matrix yet still preserve the validity of the TS policy.
  arXiv  Detail & Related papers  (2022-10-13T09:01:58Z)
• Surrogate modeling for Bayesian optimization beyond a single Gaussian process [62.294228304646516]
  We propose a novel Bayesian surrogate model to balance exploration with exploitation of the search space. To endow function sampling with scalability, random feature-based kernel approximation is leveraged per GP model. To further establish convergence of the proposed EGP-TS to the global optimum, analysis is conducted based on the notion of Bayesian regret.
  arXiv  Detail & Related papers  (2022-05-27T16:43:10Z)
• Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning [28.65635956111857]
  Federated learning (FL) enables edge devices to collaboratively train a global model without sharing their private data. To enhance the training efficiency of FL, various algorithms have been proposed, ranging from first-order computation to first-order methods.
  arXiv  Detail & Related papers  (2022-01-24T08:56:06Z)
• Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization [39.824086260578646]
  This paper presents a novel trusted-maximizers entropy search (TES) acquisition function. It measures how much an input contributes to the information gain on a query over a finite set of trusted maximizers.
  arXiv  Detail & Related papers  (2021-07-30T07:25:07Z)
• Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks [52.32646357164739]
  We propose a deep neural network (DNN) to solve the solutions of the optimal power flow (ACOPF) The proposed SIDNN is compatible with a broad range of OPF schemes. It can be seamlessly integrated in other learning-to-OPF schemes.
  arXiv  Detail & Related papers  (2021-03-27T00:45:23Z)
• Multi-Fidelity Bayesian Optimization via Deep Neural Networks [19.699020509495437]
  In many applications, the objective function can be evaluated at multiple fidelities to enable a trade-off between the cost and accuracy. We propose Deep Neural Network Multi-Fidelity Bayesian Optimization (DNN-MFBO) that can flexibly capture all kinds of complicated relationships between the fidelities. We show the advantages of our method in both synthetic benchmark datasets and real-world applications in engineering design.
  arXiv  Detail & Related papers  (2020-07-06T23:28:40Z)

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.