Personalized Federated Learning via Sequential Layer Expansion in Representation Learning

• URL: http://arxiv.org/abs/2404.17799v1
• Date: Sat, 27 Apr 2024 06:37:19 GMT
• Title: Personalized Federated Learning via Sequential Layer Expansion in Representation Learning
• Authors: Jaewon Jang, Bonjun Choi,
• Abstract summary: Federated learning ensures the privacy of clients by conducting distributed training on individual client devices and sharing only the model weights with a central server. We propose a new representation learning-based approach that suggests decoupling the entire deep learning model into more densely divided parts with the application of suitable scheduling methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning ensures the privacy of clients by conducting distributed training on individual client devices and sharing only the model weights with a central server. However, in real-world scenarios, the heterogeneity of data among clients necessitates appropriate personalization methods. In this paper, we aim to address this heterogeneity using a form of parameter decoupling known as representation learning. Representation learning divides deep learning models into 'base' and 'head' components. The base component, capturing common features across all clients, is shared with the server, while the head component, capturing unique features specific to individual clients, remains local. We propose a new representation learning-based approach that suggests decoupling the entire deep learning model into more densely divided parts with the application of suitable scheduling methods, which can benefit not only data heterogeneity but also class heterogeneity. In this paper, we compare and analyze two layer scheduling approaches, namely forward (\textit{Vanilla}) and backward (\textit{Anti}), in the context of data and class heterogeneity among clients. Our experimental results show that the proposed algorithm, when compared to existing personalized federated learning algorithms, achieves increased accuracy, especially under challenging conditions, while reducing computation costs.

Related papers

• Federated Face Forgery Detection Learning with Personalized Representation [63.90408023506508]
  Deep generator technology can produce high-quality fake videos that are indistinguishable, posing a serious social threat. Traditional forgery detection methods directly centralized training on data. The paper proposes a novel federated face forgery detection learning with personalized representation.
  arXiv  Detail & Related papers  (2024-06-17T02:20:30Z)
• Learn What You Need in Personalized Federated Learning [53.83081622573734]
  $textitLearn2pFed$ is a novel algorithm-unrolling-based personalized federated learning framework. We show that $textitLearn2pFed$ significantly outperforms previous personalized federated learning methods.
  arXiv  Detail & Related papers  (2024-01-16T12:45:15Z)
• Personalized Federated Learning via Amortized Bayesian Meta-Learning [21.126405589760367]
  We introduce a new perspective on personalized federated learning through Amortized Bayesian Meta-Learning. Specifically, we propose a novel algorithm called emphFedABML, which employs hierarchical variational inference across clients. Our theoretical analysis provides an upper bound on the average generalization error and guarantees the generalization performance on unseen data.
  arXiv  Detail & Related papers  (2023-07-05T11:58:58Z)
• Personalized Federated Learning with Feature Alignment and Classifier Collaboration [13.320381377599245]
  Data heterogeneity is one of the most challenging issues in federated learning. One such approach in deep neural networks based tasks is employing a shared feature representation and learning a customized classifier head for each client. In this work, we conduct explicit local-global feature alignment by leveraging global semantic knowledge for learning a better representation.
  arXiv  Detail & Related papers  (2023-06-20T19:58:58Z)
• FedHP: Heterogeneous Federated Learning with Privacy-preserving [0.0]
  Federated learning is a distributed machine learning environment, which ensures that clients complete collaborative training without sharing private data, only by exchanging parameters. We propose a novel federated learning method, which consists of the pre-trained model as the backbone and fully connected layers as the head. By sharing the embedding vector of classes, instead of parameters based on gradient space, clients can better adapt to private data, and it is more efficient in the communication between the server and clients.
  arXiv  Detail & Related papers  (2023-01-27T13:32:17Z)
• FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks [21.613436984547917]
  We propose a novel personalized federated learning method called federated classifier averaging (FedClassAvg) FedClassAvg aggregates weights as an agreement on decision boundaries on feature spaces. We demonstrate it outperforms the current state-of-the-art algorithms on heterogeneous personalized federated learning tasks.
  arXiv  Detail & Related papers  (2022-10-25T08:32:08Z)
• Straggler-Resilient Personalized Federated Learning [55.54344312542944]
  Federated learning allows training models from samples distributed across a large network of clients while respecting privacy and communication restrictions. We develop a novel algorithmic procedure with theoretical speedup guarantees that simultaneously handles two of these hurdles. Our method relies on ideas from representation learning theory to find a global common representation using all clients' data and learn a user-specific set of parameters leading to a personalized solution for each client.
  arXiv  Detail & Related papers  (2022-06-05T01:14:46Z)
• 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)
• Toward Understanding the Influence of Individual Clients in Federated Learning [52.07734799278535]
  Federated learning allows clients to jointly train a global model without sending their private data to a central server. We defined a new notion called em-Influence, quantify this influence over parameters, and proposed an effective efficient model to estimate this metric.
  arXiv  Detail & Related papers  (2020-12-20T14:34:36Z)
• Federated Residual Learning [53.77128418049985]
  We study a new form of federated learning where the clients train personalized local models and make predictions jointly with the server-side shared model. Using this new federated learning framework, the complexity of the central shared model can be minimized while still gaining all the performance benefits that joint training provides.
  arXiv  Detail & Related papers  (2020-03-28T19:55:24Z)

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.