Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life. However, data privacy is one of the most prominent concerns in the digital era. After several data breaches and privacy scandals, the users are now worried about sharing their data. In the last decade, Federated Learning has emerged as a new privacy-preserving distributed machine learning paradigm. It works by processing data on the user device without collecting data in a central repository. We present FedeRank (https://split.to/federank), a federated recommendation algorithm. The system learns a personal factorization model onto every device. The training of the model is a synchronous process between the central server and the federated clients. FedeRank takes care of computing recommendations in a distributed fashion and allows users to control the portion of data they want to share. By comparing with state-of-the-art algorithms, extensive experiments show the effectiveness of FedeRank in terms of recommendation accuracy, even with a small portion of shared user data. Further analysis of the recommendation lists' diversity and novelty guarantees the suitability of the algorithm in real production environments.
Recommender systems are widely used in industry to improve user experience. Despite great success, they have recently been criticized for collecting private user data. Federated Learning (FL) is a new paradigm for learning on distributed data without direct data sharing. Therefore, Federated Recommender (FedRec) systems are proposed to mitigate privacy concerns to non-distributed recommender systems. However, FedRec systems have a performance gap to its non-distributed counterpart. The main reason is that local clients have an incomplete user-item interaction graph, thus FedRec systems cannot …
Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems
Institute of Innovation, Science, and Sustainability, Federation University Australia（澳大利亚联邦大学创新、科学与可持续发展研究所）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；Institute of Electronic Information and Network Engineering, Chongqing University of Posts and Telecommunications（重庆邮电大学电子信息与网络工程研究院）；School of Computing Technologies, RMIT University（皇家墨尔本理工大学计算技术学院）
National University of Computer and Emerging Technology（国立计算机与新兴技术大学）；Huawei Technologies Oy (Finland) Co. Ltd（华为技术（芬兰）有限公司）；ComparablesAI Oy
We introduce the payload optimization method for federated recommender systems (FRS). In federated learning (FL), the global model payload that is moved between the server and users depends on the number of items to recommend. The model payload grows when there is an increasing number of items. This becomes challenging for an FRS if it is running in production mode. To tackle the payload challenge, we formulated a multi-arm bandit solution that selected part of the global model and transmitted it to all users. The selection process was guided by a novel reward function suitable for FL systems.…
EU Cloud R&D Center of Helsinki（赫尔辛基欧盟云研发中心）；Huawei Technologies（华为技术有限公司）
The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates. The model updates are sent back and aggregated on the server to update the master model then redistributed to the clients. In this paradigm, the user data never leaves the client, greatly enhancing the user' privacy, in contrast to the traditional paradigm of collecting, storing and processin…
EPFL - Swiss Federal Institute of Technology Lausanne（洛桑联邦理工学院）
Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Federated learning and decentralized learning systems address this by letting the data stay on user's machines to preserve privacy: each user performs the training on local data and only the model parameters are shared. However, sharing the model parameters across the network may still yield privacy breaches. In this paper, we present REX, the …
College of Information Science and Engineering（信息科学与工程学院）；Ningbo University（宁波大学）
Recommender System (RS) is currently an effective way to solve information overload. To meet users' next click behavior, RS needs to collect users' personal information and behavior to achieve a comprehensive and profound user preference perception. However, these centrally collected data are privacy-sensitive, and any leakage may cause severe problems to both users and service providers. This paper proposed a novel privacy-preserved recommender system framework (PPRSF), through the application of federated learning paradigm, to enable the recommendation algorithm to be trained and carry out i…
Owing to its nature of scalability and privacy by design, federated learning (FL) has received increasing interest in decentralized deep learning. FL has also facilitated recent research on upscaling and privatizing personalized recommendation services, using on-device data to learn recommender models locally. These models are then aggregated globally to obtain a more performant model, while maintaining data privacy. Typically, federated recommender systems (FRSs) do not consider the lack of resources and data availability at the end-devices. In addition, they assume that the interaction data …
Recommender Systems (RSs) have become increasingly important in many application domains, such as digital marketing. Conventional RSs often need to collect users' data, centralize them on the server-side, and form a global model to generate reliable recommendations. However, they suffer from two critical limitations: the personalization problem that the RSs trained traditionally may not be customized for individual users, and the privacy problem that directly sharing user data is not encouraged. We propose Personalized Federated Recommender Systems (PersonalFR), which introduces a personalized…
The University of Queensland（昆士兰大学）；Griffith University（格里菲斯大学）；Shandong University（山东大学）
Due to the growing privacy concerns, decentralization emerges rapidly in personalized services, especially recommendation. Also, recent studies have shown that centralized models are vulnerable to poisoning attacks, compromising their integrity. In the context of recommender systems, a typical goal of such poisoning attacks is to promote the adversary's target items by interfering with the training dataset and/or process. Hence, a common practice is to subsume recommender systems under the decentralized federated learning paradigm, which enables all user devices to collaboratively learn a glob…
In recent years, recommender systems are crucially important for the delivery of personalized services that satisfy users' preferences. With personalized recommendation services, users can enjoy a variety of recommendations such as movies, books, ads, restaurants, and more. Despite the great benefits, personalized recommendations typically require the collection of personal data for user modelling and analysis, which can make users susceptible to attribute inference attacks. Specifically, the vulnerability of existing centralized recommenders under attribute inference attacks leaves malicious …
Shandong University（山东大学）；University of Amsterdam（阿姆斯特丹大学）；Ahold Delhaize（皇家阿霍德德尔海兹集团）
Federated recommender systems have distinct advantages in terms of privacy protection over traditional recommender systems that are centralized at a data center. However, previous work on federated recommender systems does not fully consider the limitations of storage, RAM, energy and communication bandwidth in a mobile environment. The scales of the models proposed are too large to be easily run on mobile devices. And existing federated recommender systems need to fine-tune recommendation models on each device, making it hard to effectively exploit collaborative filtering information among us…
A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；City, University of London（伦敦城市大学）；Ministry of Education Key Laboratory for Intelligent Networks and Network Security, the School of Information and Communication Engineering, and SMILES LAB, Xi’an Jiaotong University（教育部智能网络与网络 security key laboratory、信息与通信工程学院及SMILES实验室，西安交通大学）
Hong Kong University of Science and Technology（香港科技大学）；Clustar；Peking University（北京大学）；Fuzhou University（福州大学）
Federated recommendation addresses the data silo and privacy problems altogether for recommender systems. Current federated recommender systems mainly utilize cryptographic or obfuscation methods to protect the original ratings from leakage. However, the former comes with extra communication and computation costs, and the latter damages model accuracy. Neither of them could simultaneously satisfy the real-time feedback and accurate personalization requirements of recommender systems. In this paper, we proposed federated masked matrix factorization (FedMMF) to protect the data privacy in federa…
Federated learning (FL) is a feasible technique to learn personalized recommendation models from decentralized user data. Unfortunately, federated recommender systems are vulnerable to poisoning attacks by malicious clients. Existing recommender system poisoning methods mainly focus on promoting the recommendation chances of target items due to financial incentives. In fact, in real-world scenarios, the attacker may also attempt to degrade the overall performance of recommender systems. However, existing general FL poisoning methods for degrading model performance are either ineffective or not…
To foster an active and engaged community, social networks employ recommendation algorithms that filter large amounts of contents and provide a user with personalized views of the network. Popular social networks such as Facebook and Twitter generate follow recommendations by listing profiles a user may be interested to connect with. Federated social networks aim to resolve issues associated with the popular social networks - such as large-scale user-surveillance and the miss-use of user data to manipulate elections - by decentralizing authority and promoting privacy. Due to their recent emerg…
Federated recommendations leverage the federated learning (FL) techniques to make privacy-preserving recommendations. Though recent success in the federated recommender system, several vital challenges remain to be addressed: (i) The majority of federated recommendation models only consider the model performance and the privacy-preserving ability, while ignoring the optimization of the communication process; (ii) Most of the federated recommenders are designed for heterogeneous systems, causing unfairness problems during the federation process; (iii) The personalization techniques have been le…
In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem. Recent deep neural network (DNN)-based recommender system research have made significant progress in improving prediction accuracy, which is largely attributed to the access to a large amount of users' personal data collected from users' devices and then centrally stored in the cloud server. However, as there are rising concerns around the globe on user privacy leakage in the online platform, the public is becoming anxio…
The marriage of federated learning and recommender system (FedRec) has been widely used to address the growing data privacy concerns in personalized recommendation services. In FedRecs, users' attribute information and behavior data (i.e., user-item interaction data) are kept locally on their personal devices, therefore, it is considered a fairly secure approach to protect user privacy. As a result, the privacy issue of FedRecs is rarely explored. Unfortunately, several recent studies reveal that FedRecs are vulnerable to user attribute inference attacks, highlighting the privacy concerns of F…
University of Maryland, Baltimore County（马里兰大学巴尔的摩郡分校）；LANL（洛斯阿拉莫斯国家实验室）
Non-negative matrix factorization (NMF) with missing-value completion is a well-known effective Collaborative Filtering (CF) method used to provide personalized user recommendations. However, traditional CF relies on the privacy-invasive collection of users' explicit and implicit feedback to build a central recommender model. One-shot federated learning has recently emerged as a method to mitigate the privacy problem while addressing the traditional communication bottleneck of federated learning. In this paper, we present the first unsupervised one-shot federated CF implementation, named FedSP…
Federated learning (FL) is an effective mechanism for data privacy in recommender systems by running machine learning model training on-device. While prior FL optimizations tackled the data and system heterogeneity challenges faced by FL, they assume the two are independent of each other. This fundamental assumption is not reflective of real-world, large-scale recommender systems -- data and system heterogeneity are tightly intertwined. This paper takes a data-driven approach to show the inter-dependence of data and system heterogeneity in real-world data and quantifies its impact on the overa…
FedFlex: Federated Learning for Diverse Netflix Recommendations
Vrije Universiteit Amsterdam Netherlands；Georgia Institute of Technology Mexico；Centrum Wiskunde \& Informatica Netherlands；Georgia Institute of Technology
FedSlate:A Federated Deep Reinforcement Learning Recommender System
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；INFLY TECH (Shanghai) Co., Ltd.（INFLY TECH（上海）有限公司）；School of Engineering, Westlake University（西湖大学工程学院）
Efficient and Robust Regularized Federated Recommendation
City University of Hong Kong（香港城市大学）；Jilin University（吉林大学）；Michigan State University（密歇根州立大学）；Wuhan University（武汉大学）；Jinan University（暨南大学）；Southern University of Science and Technology（南方科技大学）；The University of Queensland（昆士兰大学）；The Hong Kong Polytechnic University（香港理工大学）
Federated recommender system (FRS), which enables many local devices to train a shared model jointly without transmitting local raw data, has become a prevalent recommendation paradigm with privacy-preserving advantages. However, previous work on FRS performs similarity search via inner product in continuous embedding space, which causes an efficiency bottleneck when the scale of items is extremely large. We argue that such a scheme in federated settings ignores the limited capacities in resource-constrained user devices (i.e., storage space, computational overhead, and communication bandwidth…
