Find Your Friends: Personalized Federated Learning with the Right Collaborators

• URL: http://arxiv.org/abs/2210.06597v1
• Date: Wed, 12 Oct 2022 21:29:22 GMT
• Title: Find Your Friends: Personalized Federated Learning with the Right Collaborators
• Authors: Yi Sui, Junfeng Wen, Yenson Lau, Brendan Leigh Ross, Jesse C. Cresswell
• Abstract summary: In the traditional federated learning setting, a central server coordinates a network of clients to train one global model. We present a novel decentralized framework, FedeRiCo, where each client can learn as much or as little from other clients as is optimal for its local data distribution.
• Score: 7.749713014052951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the traditional federated learning setting, a central server coordinates a network of clients to train one global model. However, the global model may serve many clients poorly due to data heterogeneity. Moreover, there may not exist a trusted central party that can coordinate the clients to ensure that each of them can benefit from others. To address these concerns, we present a novel decentralized framework, FedeRiCo, where each client can learn as much or as little from other clients as is optimal for its local data distribution. Based on expectation-maximization, FedeRiCo estimates the utilities of other participants' models on each client's data so that everyone can select the right collaborators for learning. As a result, our algorithm outperforms other federated, personalized, and/or decentralized approaches on several benchmark datasets, being the only approach that consistently performs better than training with local data only.

Related papers

• FedSampling: A Better Sampling Strategy for Federated Learning [81.85411484302952]
  Federated learning (FL) is an important technique for learning models from decentralized data in a privacy-preserving way. Existing FL methods usually uniformly sample clients for local model learning in each round. We propose a novel data uniform sampling strategy for federated learning (FedSampling)
  arXiv  Detail & Related papers  (2023-06-25T13:38:51Z)
• FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution [7.924081556869144]
  We present a new algorithm, FLIS, which groups the clients population in clusters with jointly trainable data distributions. We present experimental results to demonstrate the benefits of FLIS over the state-of-the-art benchmarks on CIFAR-100/10, SVHN, and FMNIST datasets.
  arXiv  Detail & Related papers  (2022-08-20T22:10:48Z)
• Personalized Federated Learning through Local Memorization [10.925242558525683]
  Federated learning allows clients to collaboratively learn statistical models while keeping their data local. Recent personalized federated learning methods train a separate model for each client while still leveraging the knowledge available at other clients. We show on a suite of federated datasets that this approach achieves significantly higher accuracy and fairness than state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-17T19:40:07Z)
• 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)
• Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity [3.291862617649511]
  We propose a new approach for obtaining a personalized model from a client-level objective. To realize this personalization, we leverage finding a small subnetwork for each client.
  arXiv  Detail & Related papers  (2021-05-02T22:10:46Z)
• Towards Fair Federated Learning with Zero-Shot Data Augmentation [123.37082242750866]
  Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the client data. We propose a novel federated learning system that employs zero-shot data augmentation on under-represented data to mitigate statistical heterogeneity and encourage more uniform accuracy performance across clients in federated networks. We study two variants of this scheme, Fed-ZDAC (federated learning with zero-shot data augmentation at the clients) and Fed-ZDAS (federated learning with zero-shot data augmentation at the server).
  arXiv  Detail & Related papers  (2021-04-27T18:23:54Z)
• Exploiting Shared Representations for Personalized Federated Learning [54.65133770989836]
  We propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client. Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation. This result is of interest beyond federated learning to a broad class of problems in which we aim to learn a shared low-dimensional representation among data distributions.
  arXiv  Detail & Related papers  (2021-02-14T05:36:25Z)
• Personalized Federated Learning with First Order Model Optimization [76.81546598985159]
  We propose an alternative to federated learning, where each client federates with other relevant clients to obtain a stronger model per client-specific objectives. We do not assume knowledge of underlying data distributions or client similarities, and allow each client to optimize for arbitrary target distributions of interest. Our method outperforms existing alternatives, while also enabling new features for personalized FL such as transfer outside of local data distributions.
  arXiv  Detail & Related papers  (2020-12-15T19:30:29Z)
• Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification [69.29602103582782]
  Deep learning has been successful for many computer vision tasks due to the availability of shared and centralised large-scale training data. However, increasing awareness of privacy concerns poses new challenges to deep learning, especially for person re-identification (Re-ID) We propose a novel paradigm called Federated Person Re-Identification (FedReID) to construct a generalisable global model (a central server) by simultaneously learning with multiple privacy-preserved local models (local clients) This client-server collaborative learning process is iteratively performed under privacy control, enabling FedReID to realise decentralised learning without sharing distributed data nor collecting any
  arXiv  Detail & Related papers  (2020-06-07T13:32:33Z)
• Multi-Center Federated Learning [62.57229809407692]
  This paper proposes a novel multi-center aggregation mechanism for federated learning. It learns multiple global models from the non-IID user data and simultaneously derives the optimal matching between users and centers. Our experimental results on benchmark datasets show that our method outperforms several popular federated learning methods.
  arXiv  Detail & Related papers  (2020-05-03T09:14:31Z)

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.