Federated Causality Learning with Explainable Adaptive Optimization

• URL: http://arxiv.org/abs/2312.05540v1
• Date: Sat, 9 Dec 2023 11:18:20 GMT
• Title: Federated Causality Learning with Explainable Adaptive Optimization
• Authors: Dezhi Yang, Xintong He, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Jinglin Zhang
• Abstract summary: We propose a federated causal discovery strategy (FedCausal) to learn the unified global causal graph from decentralized heterogeneous data. We show that FedCausal can effectively deal with non-independently and identically distributed (non-iid) data.
• Score: 25.910766140488395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Discovering the causality from observational data is a crucial task in various scientific domains. With increasing awareness of privacy, data are not allowed to be exposed, and it is very hard to learn causal graphs from dispersed data, since these data may have different distributions. In this paper, we propose a federated causal discovery strategy (FedCausal) to learn the unified global causal graph from decentralized heterogeneous data. We design a global optimization formula to naturally aggregate the causal graphs from client data and constrain the acyclicity of the global graph without exposing local data. Unlike other federated causal learning algorithms, FedCausal unifies the local and global optimizations into a complete directed acyclic graph (DAG) learning process with a flexible optimization objective. We prove that this optimization objective has a high interpretability and can adaptively handle homogeneous and heterogeneous data. Experimental results on synthetic and real datasets show that FedCausal can effectively deal with non-independently and identically distributed (non-iid) data and has a superior performance.

Related papers

• FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning [5.23984567704876]
  Federated learning offers a paradigm to the challenge of preserving privacy in distributed machine learning. Traditional approach fails to address the phenomenon of class-wise bias in global long-tailed data. New method FedLF introduces three modifications in the local training phase: adaptive logit adjustment, continuous class centred optimization, and feature decorrelation.
  arXiv  Detail & Related papers  (2024-09-18T16:25:29Z)
• Federated Impression for Learning with Distributed Heterogeneous Data [19.50235109938016]
  Federated learning (FL) provides a paradigm that can learn from distributed datasets across clients without requiring them to share data. In FL, sub-optimal convergence is common among data from different health centers due to the variety in data collection protocols and patient demographics across centers. We propose FedImpres which alleviates catastrophic forgetting by restoring synthetic data that represents the global information as federated impression.
  arXiv  Detail & Related papers  (2024-09-11T15:37:52Z)
• 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)
• SMaRt: Improving GANs with Score Matching Regularity [94.81046452865583]
  Generative adversarial networks (GANs) usually struggle in learning from highly diverse data, whose underlying manifold is complex. We show that score matching serves as a promising solution to this issue thanks to its capability of persistently pushing the generated data points towards the real data manifold. We propose to improve the optimization of GANs with score matching regularity (SMaRt)
  arXiv  Detail & Related papers  (2023-11-30T03:05:14Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• Graph Learning Across Data Silos [12.343382413705394]
  We consider the problem of inferring graph topology from smooth graph signals in a novel but practical scenario. Data are located in distributed clients and prohibited from leaving local clients due to factors such as privacy concerns. We propose an auto-weighted multiple graph learning model to jointly learn a personalized graph for each local client and a single consensus graph for all clients.
  arXiv  Detail & Related papers  (2023-01-17T02:14:57Z)
• Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks [65.34113135080105]
  We show that not only the issue of data heterogeneity in current setups is not necessarily a problem but also in fact it can be beneficial for the FL participants. Our observations are intuitive. Our code is available at https://github.com/MMorafah/FL-SC-NIID.
  arXiv  Detail & Related papers  (2022-09-30T17:15:19Z)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)
• Federated Causal Discovery [74.37739054932733]
  This paper develops a gradient-based learning framework named DAG-Shared Federated Causal Discovery (DS-FCD) It can learn the causal graph without directly touching local data and naturally handle the data heterogeneity. Extensive experiments on both synthetic and real-world datasets verify the efficacy of the proposed method.
  arXiv  Detail & Related papers  (2021-12-07T08:04:12Z)
• Efficient Neural Causal Discovery without Acyclicity Constraints [30.08586535981525]
  We present ENCO, an efficient structure learning method for directed, acyclic causal graphs. In experiments, we show that ENCO can efficiently recover graphs with hundreds of nodes, an order of magnitude larger than what was previously possible.
  arXiv  Detail & Related papers  (2021-07-22T07:01:41Z)

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.