A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. The key component of FedALA is an Adaptive Local Aggregation (ALA) module, which can adaptively aggregate the downloaded global model and local model towards the local objective on each client to initialize the local model before training in each iteration. To evaluate the effectiveness of FedALA, we conduct extensive experiments with five benchmark datasets in computer vision and natural language processing domains. FedALA outperforms eleven state-of-the-art baselines by up to 3.27% in test accuracy. Furthermore, we also apply ALA module to other federated learning methods and achieve up to 24.19% improvement in test accuracy. Code is available at https://github.com/TsingZ0/FedALA.

Sequential recommender systems often struggle with accurate personalized recommendations due to data sparsity issues. Existing works use variational autoencoders and generative adversarial network methods to enrich sparse data. However, they often overlook diversity in the latent data distribution, hindering the model’s generative capacity. This characteristic of generative methods can introduce additional noise in many cases. Moreover, retaining personalized user preferences through the generation process remains a challenge. This work introduces DGFedRS, a Federated Recommender System Based on Diffusion Augmentation and Guided Denoising, designed to capture the diversity in the latent data distribution while preserving user-specific information and suppressing noise. In particular, we pre-train the diffusion model using the recommender dataset and use a diffusion augmentation strategy to generate interaction sequences, expanding the sparse user-item interactions in the discrete space. To preserve user-specific preferences in the generated interactions, we employ a guided denoising strategy to guide the generation process during reverse diffusion. Subsequently, we design a noise control strategy to reduce the damage to personalized information during the diffusion process. Additionally, a stepwise scheduling strategy is devised to input generated data into the sequential recommender model based on their challenge levels. The success of the DGFedRS approach is demonstrated by thorough experiments conduct on three real-world datasets.

Recently, personalized federated learning (pFL) has attracted increasing attention in privacy protection, collaborative learning, and tackling statistical heterogeneity among clients, e.g., hospitals, mobile smartphones, etc. Most existing pFL methods focus on exploiting the global information and personalized information in the client-level model parameters while neglecting that data is the source of these two kinds of information. To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global head and a personalized head, respectively. FedCP is more fine-grained to consider personalization in a sample-specific manner than existing pFL methods. Extensive experiments in computer vision and natural language processing domains show that FedCP outperforms eleven state-of-the-art methods by up to 6.69%. Furthermore, FedCP maintains its superiority when some clients accidentally drop out, which frequently happens in mobile settings. Our code is public at https://github.com/TsingZ0/FedCP.

Electronic shopping’s convenience and efficiency make it essential in modern life. In trustworthy personalized consumer recommender scenarios, diverse consumer interests lead to various interactions. Existing methods struggle to capture complex behavior dependencies and shared information across behaviors by exploring multi-behavior interaction sequences. To address this, we propose the Personalized Consumer Federated Recommender System Using Fine-grained Transformation and Hybrid Information Sharing (PCFedRec). Our contributions are as follows: Firstly, we employ the Fine-grained Transformation Module to capture fine-grained heterogeneous dependencies of consumer behaviors and model the behavior semantics of interest. Secondly, we use the Weight Correction Mechanism to reduce suboptimal position encoding and noisy input. Finally, we design the Hybrid Information Sharing Module to learn general consumer interests by leveraging common information across different behaviors. The Normalized Discounted Cumulative Gain is used to evaluate the performance of the recommender systems by considering both the relevance and rank order of the top 10 recommended items. The PCFedRec improves performance by 6.20%, 8.08%, 9.16%, and 7.14% on this metric.

The field of recommender systems aims to predict users’ latent interests by analyzing their preferences and behaviors. However, privacy concerns about user data collection lead to challenges such as incomplete initial information and data sparsity. Federated learning has emerged to address these privacy issues in recommender systems. However, federated recommender systems face heterogeneity among edge devices regarding data features and sample sizes. Moreover, differences in computational and storage capabilities introduce communication overhead and processing delays during parameter aggregation at the third-party server. This article introduces a framework named FedRL , a reinforcement learning federated recommender system for efficient communication using Reinforcement Selector and Hypernet Generator, to address the proposed issues. The Reinforcement Selector dynamically selects participating edge devices and helps to maximize their use of local data resources. Meanwhile, Hypernet Generator optimizes communication bandwidth consumption during the federated learning parameter transmission, enabling rapid deployment and updates of new model architectures or hyperparameters. Furthermore, the framework incorporates item attributes as content embeddings in edge devices’ recommender models, enriching them with global information. Real-world dataset experiments demonstrate that the proposed solution balances recommender quality and communication efficiency. The code for this work is publicly available on GitHub: https://github.com/diyicheng/FedRL .

With the arrival of the Internet of Things (IoT) era, multi-tier computing has attracted significant attention. The multi-tier computing can organize many computing devices and provide sufficient computing resources to support various IoT applications. However, due to the complex architecture and the dynamic system status of the multi-tier computing network, task offloading for multi-tier computing is still challenging. This paper proposes a novel task offloading method named OSTTD, to deal with the Offloading of Splittable Tasks with Topological Dependence in multi-tier computing networks. OSTTD formulates the task offloading as a sequential decision-making problem and learns the task offloading policy by Deep Reinforcement Learning (DRL). Compared with existing task offloading approaches, OSTTD is the first method in which the topological dependence among sub-tasks of the splittable task is fully considered. OSTTD makes offloading decisions intelligently based on the dynamic system status and can be applied to various multi-tier network topology structures. To verify the effectiveness of OSTTD, we extend and build a latency-aware multi-tier computing simulation platform. Extensive simulations show that OSTTD can significantly reduce the task processing time, thus, improving the overall task processing efficiency in multi-tier computing networks.