Modern recommendation systems operate entirely on the basis of central servers, requiring users to upload their behavior data from mobile devices to these servers. This practice has raised concerns about data privacy among users. Federated learning (FL), a machine learning technique designed to protect privacy, is becoming the standard solution. By combining federated learning with recommendation systems, Federated Recommendation Systems (FRS) allow users to collaboratively train a shared recommendation model without transmitting their original behavior data. However, existing federated learning solutions disregard the inherent limitations of resource-constrained mobile devices, which include limited storage space, computational overhead, and communication bandwidth. Although deploying a lightweight recommendation model can address the constraints of mobile devices, achieving satisfactory accuracy is difficult even with substantial communication overhead incurred during multiple rounds of federated learning training due to the limitations of the lightweight model’s capabilities and the sparsity of recommendation data. To address this issue, we propose AeroRec, a n e fficient on-device Feder ated Recommendation Framework. In AeroRec, we use federated self-supervised distillation to enhance the global model after each parameter aggregation in each round. This approach not only accelerates the convergence rate but also surpasses the upper limit of the lightweight model’s capability, thereby enabling a higher accuracy. We demonstrate that AeroRec outperforms several state-of-the-art FRS frameworks regarding recommendation accuracy and convergence speed through extensive experiments on three real-world datasets.

Federated learning (FL) enables collaborative model training over distributed computing nodes without sharing their privacy-sensitive raw data. However, in FL, iterative exchanges of model updates between distributed nodes and the cloud server can result in significant communication cost, especially when the data distributions at distributed nodes are imbalanced with requiring more rounds of iterations. In this paper, with our in-depth empirical studies, we disclose that extensive cloud aggregations can be avoided without compromising the learning accuracy if frequent aggregations can be enabled at edge network. To this end, we shed light on the hierarchical federated learning (HFL) framework, where a subset of distributed nodes can play as edge aggregators to support edge aggregations. Under the HFL framework, we formulate a communication cost minimization (CCM) problem to minimize the total communication cost required for model learning with a target accuracy by making decisions on edge aggragator selection and node-edge associations. Inspired by our data-driven insights that the potential of HFL lies in the data distribution at edge aggregators, we propose ShapeFL, i.e., SHaping dAta distRibution at Edge, to transform and solve the CCM problem. In ShapeFL, we divide the original problem into two sub-problems to minimize the per-round communication cost and maximize the data distribution diversity of edge aggregator data, respectively, and devise two light-weight algorithms to solve them accordingly. Extensive experiments are carried out based on several opened datasets and real-world network topologies, and the results demonstrate the efficacy of ShapeFL in terms of both learning accuracy and communication efficiency.

Federated learning (FL) has emerged as a popular paradigm for distributed machine learning among vast clients. Unfortunately, resource-constrained clients often fail to participate in FL because they cannot pay for the memory resources required for model training due to their limited memory or bandwidth. Split federated learning (SFL) is a novel FL framework in which clients commit intermediate results of model training to a cloud server for client-server collaborative training of models, making resource-constrained clients also eligible for FL. However, existing SFL frameworks mostly require frequent communication with the cloud server to exchange intermediate results and model parameters, which results in significant communication overhead and elongated training time. In particular, this can be exacerbated by the imbalanced data distributions of clients. To tackle this issue, we propose HSFL, a hierarchical split federated learning framework that efficiently trains SFL model through hierarchical organization participants. Under the HSFL framework, we formulate a Cloud Aggregation Time Minimization (CATM) problem to minimize the global training time and design a light-weight client assignment algorithm based on dynamic programming to solve it. Moreover, we develop a self-adaption approach to cope with the dynamic computational resources of clients. Finally, we implement and evaluate HSFL on various real-world training tasks, elaborating on its effectiveness and superiority in terms of efficiency and accuracy compared to baselines.