Co-clustering for Federated Recommender System

• URL: http://arxiv.org/abs/2411.01690v1
• Date: Sun, 03 Nov 2024 21:32:07 GMT
• Title: Co-clustering for Federated Recommender System
• Authors: Xinrui He, Shuo Liu, Jackey Keung, Jingrui He,
• Abstract summary: Federated Recommender System (FRS) offers a solution that strikes a balance between providing high-quality recommendations and preserving user privacy. The presence of statistical heterogeneity in FRS, commonly observed due to personalized decision-making patterns, can pose challenges. We propose CoFedRec, a novel Co-clustering Federated Recommendation mechanism.
• Score: 33.70723179405055
• License:
• Abstract: As data privacy and security attract increasing attention, Federated Recommender System (FRS) offers a solution that strikes a balance between providing high-quality recommendations and preserving user privacy. However, the presence of statistical heterogeneity in FRS, commonly observed due to personalized decision-making patterns, can pose challenges. To address this issue and maximize the benefit of collaborative filtering (CF) in FRS, it is intuitive to consider clustering clients (users) as well as items into different groups and learning group-specific models. Existing methods either resort to client clustering via user representations-risking privacy leakage, or employ classical clustering strategies on item embeddings or gradients, which we found are plagued by the curse of dimensionality. In this paper, we delve into the inefficiencies of the K-Means method in client grouping, attributing failures due to the high dimensionality as well as data sparsity occurring in FRS, and propose CoFedRec, a novel Co-clustering Federated Recommendation mechanism, to address clients heterogeneity and enhance the collaborative filtering within the federated framework. Specifically, the server initially formulates an item membership from the client-provided item networks. Subsequently, clients are grouped regarding a specific item category picked from the item membership during each communication round, resulting in an intelligently aggregated group model. Meanwhile, to comprehensively capture the global inter-relationships among items, we incorporate an additional supervised contrastive learning term based on the server-side generated item membership into the local training phase for each client. Extensive experiments on four datasets are provided, which verify the effectiveness of the proposed CoFedRec.

Related papers

• A Bayesian Framework for Clustered Federated Learning [14.426129993432193]
  One of the main challenges of federated learning (FL) is handling non-independent and identically distributed (non-IID) client data. We present a unified Bayesian framework for clustered FL which associates clients to clusters. This work provides insights on client-cluster associations and enables client knowledge sharing in new ways.
  arXiv  Detail & Related papers  (2024-10-20T19:11:24Z)
• Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach [49.63614966954833]
  Federated Collaborative Filtering (FedCF) is an emerging field focused on developing a new recommendation framework with preserving privacy. This paper proposes a novel personalized FedCF method by preserving users' personalized information into a latent variable and a neural model simultaneously. To effectively train the proposed framework, we model the problem as a specialized Variational AutoEncoder (VAE) task by integrating user interaction vector reconstruction with missing value prediction.
  arXiv  Detail & Related papers  (2024-08-16T05:49:14Z)
• Revealing and Utilizing In-group Favoritism for Graph-based Collaborative Filtering [9.067610121749777]
  We compute co-clusters of users and items with co-clustering algorithms and add CFworks for each cluster to extract the in-group favoritism. We experimented real world datasets considering two aspects: Finding the number of groups divided according to in-group preference, and measuring the quantity of improvement.
  arXiv  Detail & Related papers  (2024-04-23T06:43:58Z)
• FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation [69.75513501757628]
  FedGT is a novel framework for identifying malicious clients in federated learning with secure aggregation. We show that FedGT significantly outperforms the private robust aggregation approach based on the geometric median recently proposed by Pillutla et al.
  arXiv  Detail & Related papers  (2023-05-09T14:54:59Z)
• Privacy-Preserving Federated Deep Clustering based on GAN [12.256298398007848]
  We present a novel approach to Federated Deep Clustering based on Generative Adversarial Networks (GANs) Each client trains a local generative adversarial network (GAN) locally and uploads the synthetic data to the server. The server applies a deep clustering network on the synthetic data to establish $k$ cluster centroids, which are then downloaded to the clients for cluster assignment.
  arXiv  Detail & Related papers  (2022-11-30T13:20:11Z)
• FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning [91.74206675452888]
  We propose a novel method FedFM, which guides each client's features to match shared category-wise anchors. To achieve higher efficiency and flexibility, we propose a FedFM variant, called FedFM-Lite, where clients communicate with server with fewer synchronization times and communication bandwidth costs.
  arXiv  Detail & Related papers  (2022-10-14T08:11:34Z)
• Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction [76.26710990597498]
  We show that the class-imbalance of the grouped data from randomly selected clients can lead to significant performance degradation. Based on our key observation, we design an efficient client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS) In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way.
  arXiv  Detail & Related papers  (2022-09-30T05:42:56Z)
• FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation [7.621960305708476]
  We present the first unsupervised one-shot federated CF implementation, named FedSPLIT, based on NMF joint factorization. FedSPLIT can obtain similar results than the state of the art (and even outperform it in certain situations) with a substantial decrease in the number of communications.
  arXiv  Detail & Related papers  (2022-05-04T23:42:14Z)
• On the Convergence of Clustered Federated Learning [57.934295064030636]
  In a federated learning system, the clients, e.g. mobile devices and organization participants, usually have different personal preferences or behavior patterns. This paper proposes a novel weighted client-based clustered FL algorithm to leverage the client's group and each client in a unified optimization framework.
  arXiv  Detail & Related papers  (2022-02-13T02:39:19Z)
• Overcoming Data Sparsity in Group Recommendation [52.00998276970403]
  Group recommender systems should be able to accurately learn not only users' personal preferences but also preference aggregation strategy. In this paper, we take Bipartite Graphding Model (BGEM), the self-attention mechanism and Graph Convolutional Networks (GCNs) as basic building blocks to learn group and user representations in a unified way.
  arXiv  Detail & Related papers  (2020-10-02T07:11:19Z)

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.