ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning

• URL: http://arxiv.org/abs/2110.15522v1
• Date: Fri, 29 Oct 2021 03:35:15 GMT
• Title: ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning
• Authors: Maoguo Gong, Yuan Gao, Yue Wu, A.K.Qin
• Abstract summary: We propose a novel adaptive differentiable sampling framework (ADDS) for robust and communication-efficient multi-party learning. The proposed framework significantly reduces local computation and communication costs while speeding up the central model convergence.
• Score: 24.288233074516455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributed multi-party learning provides an effective approach for training a joint model with scattered data under legal and practical constraints. However, due to the quagmire of a skewed distribution of data labels across participants and the computation bottleneck of local devices, how to build smaller customized models for clients in various scenarios while providing updates appliable to the central model remains a challenge. In this paper, we propose a novel adaptive differentiable sampling framework (ADDS) for robust and communication-efficient multi-party learning. Inspired by the idea of dropout in neural networks, we introduce a network sampling strategy in the multi-party setting, which distributes different subnets of the central model to clients for updating, and the differentiable sampling rates allow each client to extract optimal local architecture from the supernet according to its private data distribution. The approach requires minimal modifications to the existing multi-party learning structure, and it is capable of integrating local updates of all subnets into the supernet, improving the robustness of the central model. The proposed framework significantly reduces local computation and communication costs while speeding up the central model convergence, as we demonstrated through experiments on real-world datasets.

Related papers

• FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning [18.38030098837294]
  Federated learning is a framework for distributed clients to collaboratively train a machine learning model using local data. We propose FedSPD, an efficient personalized federated learning algorithm for the decentralized setting. We show that FedSPD learns accurate models even in low-connectivity networks.
  arXiv  Detail & Related papers  (2024-10-24T15:48:34Z)
• 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)
• Supernet Training for Federated Image Classification under System Heterogeneity [15.2292571922932]
  In this work, we propose a novel framework to consider both scenarios, namely Federation of Supernet Training (FedSup) It is inspired by how averaging parameters in the model aggregation stage of Federated Learning (FL) is similar to weight-sharing in supernet training. Under our framework, we present an efficient algorithm (E-FedSup) by sending the sub-model to clients in the broadcast stage for reducing communication costs and training overhead.
  arXiv  Detail & Related papers  (2022-06-03T02:21:01Z)
• A Personalized Federated Learning Algorithm: an Application in Anomaly Detection [0.6700873164609007]
  Federated Learning (FL) has recently emerged as a promising method to overcome data privacy and transmission issues. In FL, datasets collected from different devices or sensors are used to train local models (clients) each of which shares its learning with a centralized model (server) This paper proposes a novel Personalized FedAvg (PC-FedAvg) which aims to control weights communication and aggregation augmented with a tailored learning algorithm to personalize the resulting models at each client.
  arXiv  Detail & Related papers  (2021-11-04T04:57:11Z)
• Decentralised Person Re-Identification with Selective Knowledge Aggregation [56.40855978874077]
  Existing person re-identification (Re-ID) methods mostly follow a centralised learning paradigm which shares all training data to a collection for model learning. Two recent works have introduced decentralised (federated) Re-ID learning for constructing a globally generalised model (server) However, these methods are poor on how to adapt the generalised model to maximise its performance on individual client domain Re-ID tasks. We present a new Selective Knowledge Aggregation approach to decentralised person Re-ID to optimise the trade-off between model personalisation and generalisation.
  arXiv  Detail & Related papers  (2021-10-21T18:09:53Z)
• Decentralized federated learning of deep neural networks on non-iid data [0.6335848702857039]
  We tackle the non-problem of learning a personalized deep learning model in a decentralized setting. We propose a method named Performance-Based Neighbor Selection (PENS) where clients with similar data detect each other and cooperate. PENS is able to achieve higher accuracies as compared to strong baselines.
  arXiv  Detail & Related papers  (2021-07-18T19:05:44Z)
• Clustered Federated Learning via Generalized Total Variation Minimization [83.26141667853057]
  We study optimization methods to train local (or personalized) models for local datasets with a decentralized network structure. Our main conceptual contribution is to formulate federated learning as total variation minimization (GTV) Our main algorithmic contribution is a fully decentralized federated learning algorithm.
  arXiv  Detail & Related papers  (2021-05-26T18:07:19Z)
• Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data [77.88594632644347]
  Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization challenge. We propose a novel momentum-based method to mitigate this decentralized training difficulty.
  arXiv  Detail & Related papers  (2021-02-09T11:27:14Z)
• A Bayesian Federated Learning Framework with Online Laplace Approximation [144.7345013348257]
  Federated learning allows multiple clients to collaboratively learn a globally shared model. We propose a novel FL framework that uses online Laplace approximation to approximate posteriors on both the client and server side. We achieve state-of-the-art results on several benchmarks, clearly demonstrating the advantages of the proposed method.
  arXiv  Detail & Related papers  (2021-02-03T08:36:58Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)
• Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters [5.319361976450981]
  Federated learning is a distributed, privacy-aware learning scenario which trains a single model on data belonging to several clients. Current federated learning methods struggle in cases with heterogeneous client-side data distributions. We propose a novel representation matching scheme that reduces the divergence of local models.
  arXiv  Detail & Related papers  (2019-12-30T20:19:20Z)

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.