Federated learning (FL) has received much attention in privacy-preserving and responsible recommender systems. Recent studies have shown promising results while federating widely used recommendation methods such as collaborative filtering. A major barrier when bringing FL into production is that the model complexity or the volume of gradients to be transmitted over the communication channel grows linearly as the number of items in a particular system increases. To address this challenge, we propose a communication-efficient neural collaborative filtering method for federated recommender systems. First, to align our solution with other deep neural architectures, we construct standard neural collaborative filtering in federated settings. Second, to solve the underlying model complexity challenge, a multi-armed bandit framework is used that intelligently selects a smaller set of payloads for each iteration of federated model training. The item selection is based on a carefully designed reward function that determines which portion of the overall payloads would be optimal for a particular user. The FL model only comprising of the selected items is transmitted over the network. The FL users train their local models in the regular federated learning way utilizing the payload-efficient global model, requiring no additional optimizations. The results show that using only 10% of the model’s payload, our method can achieve recommendation performance comparable with the standard federated neural collaborative filtering.

The rapid growth of medical imaging data presents significant challenges in diagnostic accuracy, data privacy, and computational efficiency. Traditional centralized AI models struggle with scalability and pose risks to patient confidentiality due to data aggregation. Moreover, heterogeneous medical data across institutions complicates the development of robust diagnostic tools. To address these issues, we propose MID-LLM, a novel framework that integrates Large Language Models (LLMs) with a blockchain-based federated learning system for medical image analysis. It also ensures the security and privacy of sensitive medical data across decentralized networks. MID-LLM uses verification mechanisms to ensure the global model’s integrity. It also employs aggregation techniques to reduce bias and improve training efficiency. Experiments on the BraTS 2020 dataset show that MID-LLM outperforms traditional federated learning, achieving higher Dice scores with improved computational efficiency. These results highlight MID-LLM’s potential to enhance diagnostic accuracy while offering a scalable, secure solution for AI in healthcare.

Privacy concerns in recommender systems are potentially addressed due to constitutional and commercial requirements. Centralized recommendation models are susceptible to poisoning attacks, which threaten their integrity. In this context, federated learning has emerged as an optimal solution to privacy concerns. However, recent investigations proved that Federated Recommender Systems (FedRS) are also vulnerable to model poisoning attacks. Existing attack possibilities highlighted in academic literature require a large fraction of Byzantine clients to effectively influence the training process, which is unrealistic for practical systems with millions of users. Additionally, most attack models neglected the role of the defense mechanism running at the aggregation server. To this end, we propose a novel undetectable hidden attack strategy (HidAttack) for FedRS, aiming to raise the exposure ratio of targeted items with minimum Byzantine clients. To achieve this goal, we construct a cluster of baseline attacks, on top of which a bandit model is designed that intelligently infers effective poisoned gradients. It ensures a diverse pattern of poisoned gradients and therefore, Byzantine clients cannot be distinguished from benign clients by the defense mechanism. Extensive experiments demonstrate that: 1) our attack model significantly increases the target item's exposure rate covertly without compromising the recommendation accuracy and 2) the current defenses are insufficient, emphasizing the need for better security improvements against our model poisoning attack to FedRS.

Federated Learning (FL) is a machine learning paradigm where a server trains a global model by amalgamating contributions from multiple clients, without accessing personal client data directly. Personalized Federated Learning (PFL), a specific subset of this domain, shifts focus from a global model to providing personalized models for each client. This difference in training objectives signifies that while conventional FL aims for optimal generalization at the server level, PFL focuses on client-side model personalization. Often, achieving both generalization and personalization in a model is challenging. In response, we introduce FedCACS, a Classifier Aggregation with Cosine Similarity in Federated Learning method to bridge the gap between conventional FL and PFL. On the one hand, FedCACS adopts cosine similarity and a new PFL training strategy, which enhances the personalization ability of the local model on the client and enables the model to learn more compact image representation. On the other hand, FedCACS uses a classifier aggregation module to aggregate personalized classifiers from each client to restore the generalization ability of the global model. Experiments on public datasets affirm the effectiveness of FedCACS in personalization, generalization ability, and fast adaptation.

Privacy and trust are highly demanding in practical recommendation engines. Although Federated Learning (FL) has significantly addressed privacy concerns, commercial operators are still worried about several technical challenges while bringing FL into production. In addition, classical FL has several intrinsic operational limitations such as single-point failure, data and model tampering, and heterogenic clients participating in the FL process. To address these challenges in practical recommenders, we propose a responsible recommendation generation framework based on blockchain-empowered asynchronous FL that can be adopted for any model-based recommender system. In standard FL settings, we build an additional aggregation layer in which multiple trusted nodes guided by a mediator component perform gradient aggregation to achieve an optimal model locally in a parallel fashion. The mediator partitions users into K clusters, and each cluster is represented by a cluster head. Once a cluster gets semi-global convergence, the cluster head transmits model gradients to the FL server for global aggregation. In addition the trusted cluster heads are responsible to submit the converged semi-global model to a blockchain to ensure tamper resilience. In our settings, an additional mediator component works like an independent observer that monitors the performance of each cluster head, updates a reward score, and records it into a digital ledger. Finally, evaluation results on three diversified benchmarks illustrate that the recommendation performance on selected measures is considerably comparable with the standard and federated version of a well-known neural collaborative filtering recommender.

Personalized federated learning (PFL) is a subfield of federated learning. Contrary to conventional federated learning that expects to find a general global model, PFL generates a personalized model adapted to its local data distribution for each client. Some existing PFL methods only consider improving the client-side personalization ability, discarding the server-side generalization capacity. To address this issue, we propose a fine-tuning and head aggregation method in federated learning (FedFTHA). It allows each client to maintain a personalized model head and fine-tune it after each local update to generate a local model containing the personalized head. Specifically, during FedFTHA training, these personalized heads are aggregated to generate a generalized head for the global model. FedFTHA meets the needs of both client-side model personalization and server-side model generalization. In addition, a universal optimization framework is employed to prove its convergence under convex and nonconvex conditions. We verify the personalization ability and generalization performance of FedFTHA under heterogeneous settings with benchmark data sets. The comparative analysis authenticates the significance of our proposal.

Nowadays, going out and participating in group activities is an indispensable part of human life, and group recommendation systems are needed to provide suggestions. In practice, group recommendation faces serious sparsity issues due to the lack of group-item interaction data, and the key challenge is to aggregate group member preference for group decision making. Conventional group recommendations applied a predefined strategy to aggregate the preferences of group members, which cannot model the group decision making process and do not address the data sparsity problem well. In this paper, we introduce knowledge graph into group recommendation as side information, and propose a novel end-to-end method named knowledge graph-based attentive group recommendation (KGAG) to solve the data sparsity and preference aggregation problems. Specifically, a graph convolution network (GCN) is employed to capture abundant structure information of items and users in knowledge graph to overcome the sparsity problem. Besides, to learn knowledge-aware group representation for inferring the group decision better, we capture the user-item connectivity and user-user connectivity in knowledge graph, and then adopt attention mechanism to learn the influence of each member according to user-user interaction in group and the candidate item, for member preference aggregation. Additionally, the attention mechanism can provide interpretability to group recommendation. Moreover, we extend the margin loss to our KGAG which forces the prediction score of positive item to be a distance larger than that of negative item. Experimental results show the superiority of the proposed KGAG and verify the efficacy of each component of KGAG.

Abstract Privacy protection is one of the key concerns of users in recommender system-based consumer markets. Popular recommendation frameworks such as collaborative filtering (CF) suffer from several privacy issues. Federated learning has emerged as an optimistic approach for collaborative and privacy-preserved learning. Users in a federated learning environment train a local model on a self-maintained item log and collaboratively train a global model by exchanging model parameters instead of personalized preferences. In this research, we proposed a federated learning-based privacy-preserving CF model for context-aware recommender systems that work with a user-defined collaboration protocol to ensure users’ privacy. Instead of crawling users’ personal information into a central server, the whole data are divided into two disjoint parts, i.e. user data and sharable item information. The inbuilt power of federated architecture ensures the users’ privacy concerns while providing considerably accurate recommendations. We evaluated the performance of the proposed algorithm with two publicly available datasets through both the prediction and ranking perspectives. Despite the federated cost and lack of open collaboration, the overall performance achieved through the proposed technique is comparable with popular recommendation models and satisfactory while providing significant privacy guarantees.