Federated recommenders: methods, challenges and future

Alamgir, Zareen; Khan, Farwa K.; Karim, Saira

doi:10.1007/s10586-022-03644-w

Cited by 18 publications

(6 citation statements)

References 76 publications

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“…Another factor that makes cross‐network recommendation a challenging task is the large amount of smart services' information, user‐generated data, and the data gathered by smart devices. To deal with these concerns, federated recommendation 52 and federated learning 53 models are effective means to run locally at the service providers' side, and to perform a collaborative learning and prediction based on cross‐network available data.…”

Section: Discussionmentioning

confidence: 99%

Cross‐network service recommendation in smart cities

Mezni,

Sellami,

Al‐Rasheed

et al. 2024

Concurrency and Computation

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SummaryNowadays, Internet of Things, artificial intelligence, cloud computing, and other revolutionary technologies (e.g., edge and fog computing) have become the pillar of smart cities. These latter make users' lives easier, thanks to a wide variety of smart services offered in different dimensions (e.g., smart living, smart mobility, smart economy, smart governance). However, the rapid adoption of smart services by users and the full servicelization of several cities around the world is faced with two major issues: the lack of knowledge regarding smart services' capacities (e.g., features, contextual requirements, pricing models, privacy policies, provisioning terms, etc.), and the lack of unified rating and quantification of smart services' QoS behavior. Indeed, interested users often exploit traditional search tools (e.g., Web search engines, social networks) to find and rate the needed services. This behavior has scattered the smart services' usage data (e.g., users contexts, ratings) across multiple providers platforms, which makes the search task beyond the capacity of users and, even, other service providers. Although recommender systems are a natural solution to exempt users from exploring the huge space of the offered smart services, current recommendation approaches for smart city environments are unable to deliver correct recommendations. In fact, they have been initially designed to single‐network settings (a single service repository), while smart services' consumers often are involved in multiple provider platforms. To the best of our knowledge, there exists no approach that treated smart service recommendation across multiple information networks. Therefore, the goal of this paper is to propose a cross‐network recommender system for smart cities. We first model the multiplex network of smart services' providers as a multirelational fuzzy lattice family thanks to fuzzy relational concept analysis (fuzzy RCA), which is a powerful mathematical method for data analysis and clustering. We also use the concept of anchor users to connect providers networks via the users involved in more than one provider platform. Guided by anchors' cross‐network relations, we compute the similarity between users and we define algorithms for exploring the smart services' information network, i.e. lattice family. Extensive experiments have proved the effectiveness of cross‐network recommendation and the quality of produced recommendations, compared to state‐of‐the‐art single‐network recommendation.

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Section: Discussionmentioning

confidence: 99%

Cross‐network service recommendation in smart cities

Mezni,

Sellami,

Al‐Rasheed

et al. 2024

Concurrency and Computation

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“…Meanwhile, federated learning, recognized as a decentralized and distributed machine learning method, has garnered significant attention within the realm of QoS prediction. With the federated recommenders [26], we can protect user privacy for the traditional recommenders. For example, Zhang et al [27] proposed a technique for QoS prediction employing federated matrix factorization.…”

Section: Related Workmentioning

confidence: 99%

LCSA-Fed: A low cost semi-asynchronous federated learning based on lag tolerance for services QoS prediction

Cai,

Liu,

et al. 2024

Preprint

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As a distributed training method, federated learning (FL) has been widely used in the field of quality-of-service (QoS) prediction. However, existing FL-based QoS prediction methods ignore the unreliability of end devices, which will lead to wasted training resources and high communication costs. Considering that the instability of end devices in real training environments, we propose a low cost semi-asynchronous federated learning method (LCSA-Fed) based on lag tolerance to overcome the lower convergence rate and suboptimal prediction accuracy of models. LCSA-Fed is able to reduce model communication costs and training costs by tolerating relatively lagging local models. At the same time, we employ innovations in both the user selection phase and the model aggregation phase to improve prediction accuracy while reducing overheads. By conducting relevant validation experiments on a publicly available QoS dataset, we conclude that our model LCSA-Fed can effectively reduce overhead and improve prediction accuracy.

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“…In fact, there are numerous methods in the literature to incorporate current RecSys frameworks into FL. They may be classified as either focusing on learning algorithms [2], security [64], or optimization models [57], depending on the task's objective [1]. Matrix factorization is a commonly utilized approach in the first scenario.…”

Section: Federated Recommender Systemsmentioning

confidence: 99%

“…For example, some users rated an infeasible number of movies in a single day, while other users had an impossibly high number of total ratings. Therefore, the MovieLens 25M dataset was inspected more closely in terms of four different metrics: (1) average times between ratings of all users David Neumann, Andreas Lutz, Karsten Müller, and Wojciech Samek in the dataset, i.e., the speed at which users have rated movies, (2) number of ratings per user, (3) number of ratings per movie, and (4) number of ratings cast by rating value. The results are shown in Figure 5.…”

Section: Dataset Analysismentioning

confidence: 99%

“…Furthermore, FL helps to distribute the burden of data storage and the computational overhead of training among many clients. On the contrary, FL also has the following disadvantages: (1) training time will be increased as compared to traditional central training, because client devices are less capable and not always available, (2) non-independent and identically distributed (i.i.d.) data can hinder convergence and result in a model with lower performance than its centrally trained counterpart, (3) battery usage of mobile client devices will increase due to the complex computations required to train the model, resulting in Manuscript submitted to ACM shorter battery lives, and (4) the communication overhead of continuously exchanging training updates between the clients and the central server, which is especially problematic when client's are on a metered mobile connection.…”

Section: Introductionmentioning

confidence: 99%

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A Privacy Preserving System for Movie Recommendations using Federated Learning

Neumann¹,

Lutz²,

Müller³

et al. 2023

Preprint

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Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are employed by many online businesses to drive engagement and sales. Besides other criticisms, like creating filter bubbles within social networks, recommender systems are often reproved for collecting considerable amounts of personal data. However, to personalize recommendations, personal information is fundamentally required. A recent distributed learning scheme called federated learning has made it possible to learn from personal user data without its central collection. Accordingly, we present a complete recommender system for movie recommendations, which provides privacy and thus trustworthiness on two levels: First, it is trained using federated learning and thus is, by its very nature, privacy-preserving, while still enabling individual users to benefit from global insights. And second, a novel federated learning scheme, FedQ, is employed, which not only addresses the problem of non-i.i.d. and small local datasets, but also prevents input data reconstruction attacks by aggregating client models early. To reduce the communication overhead, compression is applied, which significantly reduces the exchanged neural network updates to a fraction of their original data. We conjecture that it may also improve data privacy through its lossy quantization stage.

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Federated recommenders: methods, challenges and future

Cited by 18 publications

References 76 publications

Cross‐network service recommendation in smart cities

Cross‐network service recommendation in smart cities

LCSA-Fed: A low cost semi-asynchronous federated learning based on lag tolerance for services QoS prediction

A Privacy Preserving System for Movie Recommendations using Federated Learning

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