A Post-Processing-Based Fair Federated Learning Framework

• URL: http://arxiv.org/abs/2501.15318v1
• Date: Sat, 25 Jan 2025 20:05:27 GMT
• Title: A Post-Processing-Based Fair Federated Learning Framework
• Authors: Yi Zhou, Naman Goel,
• Abstract summary: Federated Learning (FL) allows collaborative model training among distributed parties without pooling local datasets at a central server.<n>We define and empirically analyze a simple and intuitive post-processing-based framework to improve group fairness in FL systems.<n>Our work shows that this framework not only simplifies fairness implementation in FL but also provides significant fairness improvements with minimal accuracy loss or even accuracy gain.
• Score: 8.978878439498365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) allows collaborative model training among distributed parties without pooling local datasets at a central server. However, the distributed nature of FL poses challenges in training fair federated learning models. The existing techniques are often limited in offering fairness flexibility to clients and performance. We formally define and empirically analyze a simple and intuitive post-processing-based framework to improve group fairness in FL systems. This framework can be divided into two stages: a standard FL training stage followed by a completely decentralized local debiasing stage. In the first stage, a global model is trained without fairness constraints using a standard federated learning algorithm (e.g. FedAvg). In the second stage, each client applies fairness post-processing on the global model using their respective local dataset. This allows for customized fairness improvements based on clients' desired and context-guided fairness requirements. We demonstrate two well-established post-processing techniques in this framework: model output post-processing and final layer fine-tuning. We evaluate the framework against three common baselines on four different datasets, including tabular, signal, and image data, each with varying levels of data heterogeneity across clients. Our work shows that this framework not only simplifies fairness implementation in FL but also provides significant fairness improvements with minimal accuracy loss or even accuracy gain, across data modalities and machine learning methods, being especially effective in more heterogeneous settings.

Related papers

• pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning [17.879602968559198]
  Federated learning algorithms aim to maximize clients' accuracy by training a model on their collective data. Group fairness constraints can be incorporated into the objective function of the FL optimization problem. We show that such an approach would lead to suboptimal classification accuracy in an FL setting with heterogeneous client distributions.
  arXiv  Detail & Related papers  (2025-03-19T06:15:31Z)
• FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning [12.713572267830658]
  textttFedTilt is a novel FL that can preserve multi-level fairness and be robust to outliers. We show how tuning tilt values can achieve the two-level fairness and mitigate the persistent outliers.
  arXiv  Detail & Related papers  (2025-03-15T19:57:23Z)
• Client-Centric Federated Adaptive Optimization [78.30827455292827]
  Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private.<n>We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
  arXiv  Detail & Related papers  (2025-01-17T04:00:50Z)
• WassFFed: Wasserstein Fair Federated Learning [31.135784690264888]
  Federated Learning (FL) employs a training approach to address scenarios where users' data cannot be shared across clients. We propose a Wasserstein Fair Federated Learning framework, namely WassFFed.
  arXiv  Detail & Related papers  (2024-11-11T11:26:22Z)
• Personalized Federated Learning via Feature Distribution Adaptation [3.410799378893257]
  Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model. personalized federated learning (PFL) seeks to address this by learning individual models tailored to each client. We propose an algorithm, pFedFDA, that efficiently generates personalized models by adapting global generative classifiers to their local feature distributions.
  arXiv  Detail & Related papers  (2024-11-01T03:03:52Z)
• 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)
• 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)
• Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning [9.975023463908496]
  Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. We propose a novel regularization technique based on adaptive self-distillation (ASD) for training models on the client side. Our regularization scheme adaptively adjusts to the client's training data based on the global model entropy and the client's label distribution.
  arXiv  Detail & Related papers  (2023-05-31T07:00:42Z)
• Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training [67.67045085186797]
  Almost all existing systems have to face large communication burdens if the central FL server fails. It personalizes the "right" in the deep models by alternately updating the shared and personal parameters. To further promote the shared parameters aggregation process, we propose DFed integrating the local Sharpness Miniization.
  arXiv  Detail & Related papers  (2023-05-24T13:52:18Z)
• Visual Prompt Based Personalized Federated Learning [83.04104655903846]
  We propose a novel PFL framework for image classification tasks, dubbed pFedPT, that leverages personalized visual prompts to implicitly represent local data distribution information of clients. Experiments on the CIFAR10 and CIFAR100 datasets show that pFedPT outperforms several state-of-the-art (SOTA) PFL algorithms by a large margin in various settings.
  arXiv  Detail & Related papers  (2023-03-15T15:02:15Z)
• FL Games: A Federated Learning Framework for Distribution Shifts [71.98708418753786]
  Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. We propose FL GAMES, a game-theoretic framework for federated learning that learns causal features that are invariant across clients.
  arXiv  Detail & Related papers  (2022-10-31T22:59:03Z)
• Federated Multi-Task Learning under a Mixture of Distributions [10.00087964926414]
  Federated Learning (FL) is a framework for on-device collaborative training of machine learning models. First efforts in FL focused on learning a single global model with good average performance across clients, but the global model may be arbitrarily bad for a given client. We study federated MTL under the flexible assumption that each local data distribution is a mixture of unknown underlying distributions.
  arXiv  Detail & Related papers  (2021-08-23T15:47:53Z)

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.