Federated Communication-Efficient Multi-Objective Optimization

• URL: http://arxiv.org/abs/2410.16398v1
• Date: Mon, 21 Oct 2024 18:09:22 GMT
• Title: Federated Communication-Efficient Multi-Objective Optimization
• Authors: Baris Askin, Pranay Sharma, Gauri Joshi, Carlee Joe-Wong,
• Abstract summary: We propose FedCMOO, a novel communication- federated multiobjective (FMOO) algorithm that improves the error convergence performance of the model compared to existing approaches. In addition, we introduce a variant of FedCMOO that allows users to specify a gradient over the objectives in terms of a desired ratio of the final objective values.
• Score: 27.492821176616815
• License:
• Abstract: We study a federated version of multi-objective optimization (MOO), where a single model is trained to optimize multiple objective functions. MOO has been extensively studied in the centralized setting but is less explored in federated or distributed settings. We propose FedCMOO, a novel communication-efficient federated multi-objective optimization (FMOO) algorithm that improves the error convergence performance of the model compared to existing approaches. Unlike prior works, the communication cost of FedCMOO does not scale with the number of objectives, as each client sends a single aggregated gradient, obtained using randomized SVD (singular value decomposition), to the central server. We provide a convergence analysis of the proposed method for smooth non-convex objective functions under milder assumptions than in prior work. In addition, we introduce a variant of FedCMOO that allows users to specify a preference over the objectives in terms of a desired ratio of the final objective values. Through extensive experiments, we demonstrate the superiority of our proposed method over baseline approaches.

Related papers

• Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate [105.86576388991713]
  We introduce a normalized gradient difference (NGDiff) algorithm, enabling us to have better control over the trade-off between the objectives. We provide a theoretical analysis and empirically demonstrate the superior performance of NGDiff among state-of-the-art unlearning methods on the TOFU and MUSE datasets.
  arXiv  Detail & Related papers  (2024-10-29T14:41:44Z)
• An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting [53.36437745983783]
  We first construct a max-margin optimization-based model to model potentially non-monotonic preferences. We devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration. Two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences.
  arXiv  Detail & Related papers  (2024-09-04T14:36:20Z)
• Decoding-Time Language Model Alignment with Multiple Objectives [116.42095026960598]
  Existing methods primarily focus on optimizing LMs for a single reward function, limiting their adaptability to varied objectives. Here, we propose $textbfmulti-objective decoding (MOD)$, a decoding-time algorithm that outputs the next token from a linear combination of predictions. We show why existing approaches can be sub-optimal even in natural settings and obtain optimality guarantees for our method.
  arXiv  Detail & Related papers  (2024-06-27T02:46:30Z)
• UCB-driven Utility Function Search for Multi-objective Reinforcement Learning [75.11267478778295]
  In Multi-objective Reinforcement Learning (MORL) agents are tasked with optimising decision-making behaviours. We focus on the case of linear utility functions parameterised by weight vectors w. We introduce a method based on Upper Confidence Bound to efficiently search for the most promising weight vectors during different stages of the learning process.
  arXiv  Detail & Related papers  (2024-05-01T09:34:42Z)
• 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)
• BOtied: Multi-objective Bayesian optimization with tied multivariate ranks [33.414682601242006]
  In this paper, we show a natural connection between non-dominated solutions and the extreme quantile of the joint cumulative distribution function. Motivated by this link, we propose the Pareto-compliant CDF indicator and the associated acquisition function, BOtied. Our experiments on a variety of synthetic and real-world problems demonstrate that BOtied outperforms state-of-the-art MOBO acquisition functions.
  arXiv  Detail & Related papers  (2023-06-01T04:50:06Z)
• Joint Entropy Search for Multi-objective Bayesian Optimization [0.0]
  We propose a novel information-theoretic acquisition function for BO called Joint Entropy Search. We showcase the effectiveness of this new approach on a range of synthetic and real-world problems in terms of the hypervolume and its weighted variants.
  arXiv  Detail & Related papers  (2022-10-06T13:19:08Z)
• R-MBO: A Multi-surrogate Approach for Preference Incorporation in Multi-objective Bayesian Optimisation [0.0]
  We present an a-priori multi-surrogate approach to incorporate the desirable objective function values as the preferences of a decision-maker in multi-objective BO. The results and comparison with the existing mono-surrogate approach on benchmark and real-world optimisation problems show the potential of the proposed approach.
  arXiv  Detail & Related papers  (2022-04-27T19:58:26Z)
• A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization [11.92436948211501]
  This paper proposes a federated data-driven evolutionary multi-objective/many-objective optimization algorithm. We leverage federated learning for surrogate construction so that multiple clients collaboratively train a radial-basis-function-network as the global surrogate. A new federated acquisition function is proposed for the central server to approximate the objective values using the global surrogate and estimate the uncertainty level of the approximated objective values.
  arXiv  Detail & Related papers  (2021-06-22T22:33:24Z)
• Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach [44.25245545568633]
  We study the novel problem of blackbox optimization of multiple objectives via multi-fidelity function evaluations. Our experiments on several synthetic and real-world benchmark problems show that MF-OSEMO, with both approximations, significantly improves over the state-of-the-art single-fidelity algorithms.
  arXiv  Detail & Related papers  (2020-11-02T06:59:04Z)
• Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization [93.78811018928583]
  This paper provides a framework to analyze the convergence of federated heterogeneous optimization algorithms. We propose FedNova, a normalized averaging method that eliminates objective inconsistency while preserving fast error convergence.
  arXiv  Detail & Related papers  (2020-07-15T05:01:23Z)

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.