FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation

• URL: http://arxiv.org/abs/2504.14208v1
• Date: Sat, 19 Apr 2025 06:59:34 GMT
• Title: FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation
• Authors: Mingzhe Han, Dongsheng Li, Jiafeng Xia, Jiahao Liu, Hansu Gu, Peng Zhang, Ning Gu, Tun Lu,
• Abstract summary: We introduce the federated collaborative information aggregation (FedCIA) method for privacy-preserving recommendation.<n>FedCIA allows clients to align their local models without constraining embeddings to a unified vector space.<n>It mitigates information loss caused by direct summation, preserves the personalized embedding distributions of individual clients, and supports the aggregation of parameter-free models.
• Score: 28.8047308546416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommendation algorithms rely on user historical interactions to deliver personalized suggestions, which raises significant privacy concerns. Federated recommendation algorithms tackle this issue by combining local model training with server-side model aggregation, where most existing algorithms use a uniform weighted summation to aggregate item embeddings from different client models. This approach has three major limitations: 1) information loss during aggregation, 2) failure to retain personalized local features, and 3) incompatibility with parameter-free recommendation algorithms. To address these limitations, we first review the development of recommendation algorithms and recognize that their core function is to share collaborative information, specifically the global relationship between users and items. With this understanding, we propose a novel aggregation paradigm named collaborative information aggregation, which focuses on sharing collaborative information rather than item parameters. Based on this new paradigm, we introduce the federated collaborative information aggregation (FedCIA) method for privacy-preserving recommendation. This method requires each client to upload item similarity matrices for aggregation, which allows clients to align their local models without constraining embeddings to a unified vector space. As a result, it mitigates information loss caused by direct summation, preserves the personalized embedding distributions of individual clients, and supports the aggregation of parameter-free models. Theoretical analysis and experimental results on real-world datasets demonstrate the superior performance of FedCIA compared with the state-of-the-art federated recommendation algorithms. Code is available at https://github.com/Mingzhe-Han/FedCIA.

Related papers

• PDC-FRS: Privacy-preserving Data Contribution for Federated Recommender System [15.589541738576528]
  Federated recommender systems (FedRecs) have emerged as a popular research direction for protecting users' privacy in on-device recommendations. In FedRecs, users keep their data locally and only contribute their local collaborative information by uploading model parameters to a central server. We propose a novel federated recommendation framework, PDC-FRS. Specifically, we design a privacy-preserving data contribution mechanism that allows users to share their data with a differential privacy guarantee.
  arXiv  Detail & Related papers  (2024-09-12T06:13:07Z)
• 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.<n>Existing FedCF methods typically combine distributed Collaborative Filtering (CF) algorithms with privacy-preserving mechanisms, and then preserve personalized information into a user embedding vector.<n>This paper proposes a novel personalized FedCF method by preserving users' personalized information into a latent variable and a neural model simultaneously.
  arXiv  Detail & Related papers  (2024-08-16T05:49:14Z)
• 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)
• Beyond Similarity: Personalized Federated Recommendation with Composite Aggregation [22.359428566363945]
  Federated recommendation aims to collect global knowledge by aggregating local models from massive devices. Current methods mainly leverage aggregation functions invented by federated vision community to aggregate parameters from similar clients. We propose a personalized Federated recommendation model with Composite Aggregation (FedCA)
  arXiv  Detail & Related papers  (2024-06-06T10:17:52Z)
• FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancement [20.214339212091012]
  Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recent Graph Neural Networks (GNN) have gained popularity in recommendation tasks due to their ability to capture high-order interactions between users and items. We propose FedRKG, a novel federated recommendation system, where a global knowledge graph (KG) is constructed and maintained on the server using publicly available item information.
  arXiv  Detail & Related papers  (2024-01-20T02:38:21Z)
• 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)
• APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation [86.29366168836141]
  We propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR) APGL4SR incorporates adaptive and personalized global collaborative information into sequential recommendation systems. As a generic framework, APGL4SR can outperform other baselines with significant margins.
  arXiv  Detail & Related papers  (2023-11-06T01:33:24Z)
• Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation [45.39171059168941]
  heterogeneous information network (HIN) is a potent tool for mitigating data sparsity in recommender systems. In this paper, we suggest the HIN is partitioned into private HINs stored on the client side and shared HINs on the server. We formalize the privacy definition for HIN-based federated recommendation (FedRec) in the light of differential privacy.
  arXiv  Detail & Related papers  (2023-10-18T05:59:41Z)
• Client-specific Property Inference against Secure Aggregation in Federated Learning [52.8564467292226]
  Federated learning has become a widely used paradigm for collaboratively training a common model among different participants. Many attacks have shown that it is still possible to infer sensitive information such as membership, property, or outright reconstruction of participant data. We show that simple linear models can effectively capture client-specific properties only from the aggregated model updates.
  arXiv  Detail & Related papers  (2023-03-07T14:11:01Z)
• 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)
• 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)
• An Expectation-Maximization Perspective on Federated Learning [75.67515842938299]
  Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device. In this work, we view the server-orchestrated federated learning process as a hierarchical latent variable model where the server provides the parameters of a prior distribution over the client-specific model parameters. We show that with simple Gaussian priors and a hard version of the well known Expectation-Maximization (EM) algorithm, learning in such a model corresponds to FedAvg, the most popular algorithm for the federated learning setting.
  arXiv  Detail & Related papers  (2021-11-19T12:58:59Z)

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.