Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation

• URL: http://arxiv.org/abs/2412.10436v1
• Date: Wed, 11 Dec 2024 08:10:46 GMT
• Title: Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation
• Authors: SeungBum Ha, Taehwan Lee, Jiyoun Lim, Sung Whan Yoon,
• Abstract summary: Federated learning (FL) has recently garnered attention as a data-decentralized training framework. We propose a benchmark process to establish an FL benchmark with controllable semantic heterogeneity across clients. As a proof of concept, we first construct a federated PSG benchmark, demonstrating the efficacy of the existing PSG methods in an FL setting.
• Score: 3.499870393443268
• License:
• Abstract: Federated learning (FL) has recently garnered attention as a data-decentralized training framework that enables the learning of deep models from locally distributed samples while keeping data privacy. Built upon the framework, immense efforts have been made to establish FL benchmarks, which provide rigorous evaluation settings that control data heterogeneity across clients. Prior efforts have mainly focused on handling relatively simple classification tasks, where each sample is annotated with a one-hot label, such as MNIST, CIFAR, LEAF benchmark, etc. However, little attention has been paid to demonstrating an FL benchmark that handles complicated semantics, where each sample encompasses diverse semantic information from multiple labels, such as Panoptic Scene Graph Generation (PSG) with objects, subjects, and relations between them. Because the existing benchmark is designed to distribute data in a narrow view of a single semantic, e.g., a one-hot label, managing the complicated semantic heterogeneity across clients when formalizing FL benchmarks is non-trivial. In this paper, we propose a benchmark process to establish an FL benchmark with controllable semantic heterogeneity across clients: two key steps are i) data clustering with semantics and ii) data distributing via controllable semantic heterogeneity across clients. As a proof of concept, we first construct a federated PSG benchmark, demonstrating the efficacy of the existing PSG methods in an FL setting with controllable semantic heterogeneity of scene graphs. We also present the effectiveness of our benchmark by applying robust federated learning algorithms to data heterogeneity to show increased performance. Our code is available at https://github.com/Seung-B/FL-PSG.

Related papers

• Boosting Federated Learning with FedEntOpt: Mitigating Label Skew by Entropy-Based Client Selection [13.851391819710367]
  Deep learning domains typically require an extensive amount of data for optimal performance. FedEntOpt is designed to mitigate performance issues caused by label distribution skew. It exhibits robust and superior performance in scenarios with low participation rates and client dropout.
  arXiv  Detail & Related papers  (2024-11-02T13:31:36Z)
• Federated Contrastive Learning for Personalized Semantic Communication [55.46383524190467]
  We design a federated contrastive learning framework aimed at supporting personalized semantic communication. FedCL enables collaborative training of local semantic encoders across multiple clients and a global semantic decoder owned by the base station. To tackle the semantic imbalance issue arising from heterogeneous datasets across distributed clients, we employ contrastive learning to train a semantic centroid generator.
  arXiv  Detail & Related papers  (2024-06-13T14:45:35Z)
• 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)
• FLASH: Federated Learning Across Simultaneous Heterogeneities [54.80435317208111]
  FLASH(Federated Learning Across Simultaneous Heterogeneities) is a lightweight and flexible client selection algorithm. It outperforms state-of-the-art FL frameworks under extensive sources of Heterogeneities. It achieves substantial and consistent improvements over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-13T20:04:39Z)
• AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity [44.11777886421429]
  Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks. We introduce the concept of structure Non-iid split and then present a new paradigm called underlineAdaptive underlineFederated underlineGraph underlineLearning (AdaFGL) Our proposed AdaFGL outperforms baselines by significant margins of 3.24% and 5.57% on community split and structure Non-iid split, respectively.
  arXiv  Detail & Related papers  (2024-01-22T08:23:31Z)
• Exploiting Label Skews in Federated Learning with Model Concatenation [39.38427550571378]
  Federated Learning (FL) has emerged as a promising solution to perform deep learning on different data owners without exchanging raw data. Among different non-IID types, label skews have been challenging and common in image classification and other tasks. We propose FedConcat, a simple and effective approach that degrades these local models as the base of the global model.
  arXiv  Detail & Related papers  (2023-12-11T10:44:52Z)
• Fake It Till Make It: Federated Learning with Consensus-Oriented Generation [52.82176415223988]
  We propose federated learning with consensus-oriented generation (FedCOG) FedCOG consists of two key components at the client side: complementary data generation and knowledge-distillation-based model training. Experiments on classical and real-world FL datasets show that FedCOG consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-10T18:49:59Z)
• A Diffusion Weighted Graph Framework for New Intent Discovery [25.364554033681515]
  New Intent Discovery (NID) aims to recognize both new and known intents from unlabeled data. Previous methods generate noisy supervisory signals which cannot strike a balance between quantity and quality. We propose a novel Diffusion Weighted Graph Framework (DWGF) to capture both semantic similarities and structure relationships inherent in data.
  arXiv  Detail & Related papers  (2023-10-24T13:43:01Z)
• PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning [55.930403371398114]
  We propose a novel generative adversarial network (GAN) sharing and aggregation strategy for personalized learning (PFL) PFL-GAN addresses the client heterogeneity in different scenarios. More specially, we first learn the similarity among clients and then develop an weighted collaborative data aggregation. The empirical results through the rigorous experimentation on several well-known datasets demonstrate the effectiveness of PFL-GAN.
  arXiv  Detail & Related papers  (2023-08-23T22:38:35Z)
• Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces [59.33965805898736]
  Clustered learning has been shown to produce promising results by grouping clients into clusters. Existing FL algorithms are essentially trying to group clients together with similar distributions. Prior FL algorithms attempt similarities indirectly during training.
  arXiv  Detail & Related papers  (2022-09-21T17:37:54Z)

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.