ProFL: Performative Robust Optimal Federated Learning

• URL: http://arxiv.org/abs/2410.18075v2
• Date: Thu, 06 Nov 2025 19:22:38 GMT
• Title: ProFL: Performative Robust Optimal Federated Learning
• Authors: Xue Zheng, Tian Xie, Xuwei Tan, Aylin Yener, Xueru Zhang,
• Abstract summary: Performative prediction is a convex distribution shift that occurs during the training of machine learning models.<n>This paper proposes Performative Learning algorithm that finds performative optimal points in federated learning.
• Score: 20.54517189040872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Performative prediction is a framework that captures distribution shifts that occur during the training of machine learning models due to their deployment. As the trained model is used, data generation causes the model to evolve, leading to deviations from the original data distribution. The impact of such model-induced distribution shifts in federated learning is increasingly likely to transpire in real-life use cases. A recently proposed approach extends performative prediction to federated learning with the resulting model converging to a performative stable point, which may be far from the performative optimal point. Earlier research in centralized settings has shown that the performative optimal point can be achieved under model-induced distribution shifts, but these approaches require the performative risk to be convex and the training data to be noiseless, assumptions often violated in realistic federated learning systems. This paper overcomes all of these shortcomings and proposes Performative Robust Optimal Federated Learning, an algorithm that finds performative optimal points in federated learning from noisy and contaminated data. We present the convergence analysis under the Polyak-Lojasiewicz condition, which applies to non-convex objectives. Extensive experiments on multiple datasets demonstrate the advantage of Robust Optimal Federated Learning over the state-of-the-art.

Related papers

• Distributionally Robust Federated Learning with Outlier Resilience [8.69285602685459]
  We study distributionally robust federated learning with explicit outlier resilience.<n>We reformulate the problem as a tractable Lagrangian penalty optimization, which admits robustness certificates.<n>Building on this reformulation, we propose the distributionally outlier-robust federated learning algorithm and establish its convergence guarantees.
  arXiv  Detail & Related papers  (2025-09-29T08:42:12Z)
• Federated Online Learning for Heterogeneous Multisource Streaming Data [0.0]
  Federated learning has emerged as an essential paradigm for distributed multi-source data analysis under privacy concerns.<n>In this paper, we propose a federated online learning (FOL) method for distributed multi-source streaming data analysis.
  arXiv  Detail & Related papers  (2025-08-08T19:08:53Z)
• SPaRFT: Self-Paced Reinforcement Fine-Tuning for Large Language Models [51.74498855100541]
  Large language models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL)<n>We propose textbfSPaRFT, a self-paced learning framework that enables efficient learning based on the capability of the model being trained.
  arXiv  Detail & Related papers  (2025-08-07T03:50:48Z)
• Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data [35.47385526394076]
  Fine-tuning pre-trained models is a popular approach in machine learning for solving complex tasks with moderate data.<n>Fine-tuning the entire pre-trained model is ineffective in federated data scenarios where local data distributions are diversely skewed.<n>Our approach transforms federated learning into a distributed set modeling task, aggregating diverse sets of prompts to globally fine-tune the pre-trained model.
  arXiv  Detail & Related papers  (2025-02-27T04:31:34Z)
• A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training Loops [55.07063067759609]
  High-quality data is essential for training large generative models, yet the vast reservoir of real data available online has become nearly depleted.<n>Models increasingly generate their own data for further training, forming Self-consuming Training Loops (STLs)<n>Some models degrade or even collapse, while others successfully avoid these failures, leaving a significant gap in theoretical understanding.
  arXiv  Detail & Related papers  (2025-02-26T06:18:13Z)
• Feasible Learning [78.6167929413604]
  We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bounds the loss for each training sample. Our empirical analysis, spanning image classification, age regression, and preference optimization in large language models, demonstrates that models trained via FL can learn from data while displaying improved tail behavior compared to ERM, with only a marginal impact on average performance.
  arXiv  Detail & Related papers  (2025-01-24T20:39:38Z)
• Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data [39.40116554523575]
  We present Drift-Resilient TabPFN, a fresh approach based on In-Context Learning with a Prior-Data Fitted Network. It learns to approximate Bayesian inference on synthetic datasets drawn from a prior. It improves accuracy from 0.688 to 0.744 and ROC AUC from 0.786 to 0.832 while maintaining stronger calibration.
  arXiv  Detail & Related papers  (2024-11-15T23:49:23Z)
• Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning? [50.03434441234569]
  Federated Learning (FL) has gained significant popularity due to its effectiveness in training machine learning models across diverse sites without requiring direct data sharing. While various algorithms have shown that FL with local updates is a communication-efficient distributed learning framework, the generalization performance of FL with local updates has received comparatively less attention.
  arXiv  Detail & Related papers  (2024-09-05T19:00:18Z)
• Rejection via Learning Density Ratios [50.91522897152437]
  Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance. Our framework is tested empirically over clean and noisy datasets.
  arXiv  Detail & Related papers  (2024-05-29T01:32:17Z)
• Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos [11.443755718706562]
  Federated learning, aimed at leveraging vast distributed datasets, confronts a crucial challenge: the heterogeneity of data across different silos.<n>We propose an innovative yet straightforward iterative framework, termed emphUncertainty-Based Extensible-Codebook Federated Learning (UEFL).<n>This framework dynamically maps latent features to trainable discrete vectors, assesses the uncertainty, and specifically extends the discretization dictionary or codebook for silos exhibiting high uncertainty.
  arXiv  Detail & Related papers  (2024-02-29T06:13:10Z)
• 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)
• Tackling Computational Heterogeneity in FL: A Few Theoretical Insights [68.8204255655161]
  We introduce and analyse a novel aggregation framework that allows for formalizing and tackling computational heterogeneous data. Proposed aggregation algorithms are extensively analyzed from a theoretical, and an experimental prospective.
  arXiv  Detail & Related papers  (2023-07-12T16:28:21Z)
• 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)
• Federated Latent Class Regression for Hierarchical Data [5.110894308882439]
  Federated Learning (FL) allows a number of agents to participate in training a global machine learning model without disclosing locally stored data. We propose a novel probabilistic model, Hierarchical Latent Class Regression (HLCR), and its extension to Federated Learning, FEDHLCR. Our inference algorithm, being derived from Bayesian theory, provides strong convergence guarantees and good robustness to overfitting. Experimental results show that FEDHLCR offers fast convergence even in non-IID datasets.
  arXiv  Detail & Related papers  (2022-06-22T00:33:04Z)
• FedCAT: Towards Accurate Federated Learning via Device Concatenation [4.416919766772866]
  Federated Learning (FL) enables all the involved devices to train a global model collaboratively without exposing their local data privacy. For non-IID scenarios, the classification accuracy of FL models decreases drastically due to the weight divergence caused by data heterogeneity. We introduce a novel FL approach named Fed-Cat that can achieve high model accuracy based on our proposed device selection strategy and device concatenation-based local training method.
  arXiv  Detail & Related papers  (2022-02-23T10:08:43Z)
• How to Learn when Data Gradually Reacts to Your Model [10.074466859579571]
  We propose a new algorithm, Stateful Performative Gradient Descent (Stateful PerfGD), for minimizing the performative loss even in the presence of these effects. Our experiments confirm that Stateful PerfGD substantially outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-13T22:05:26Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)
• Prototype Guided Federated Learning of Visual Feature Representations [15.021124010665194]
  Federated Learning (FL) is a framework which enables distributed model training using a large corpus of decentralized training data. Existing methods aggregate models disregarding their internal representations, which are crucial for training models in vision tasks. We introduce FedProto, which computes client deviations using margins of representations learned on distributed data.
  arXiv  Detail & Related papers  (2021-05-19T08:29:12Z)
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data [66.78671826743884]
  The distributionally robust optimization framework is considered for training a parametric model. The objective is to endow the trained model with robustness against adversarially manipulated input data. Proposed algorithms offer robustness with little overhead.
  arXiv  Detail & Related papers  (2020-07-07T18:25:25Z)

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.