We present a real-time indoor visible light positioning system based on optical camera communication, where the coordinate data in the on and off keying format is transmitted via light emitting diode (LED) based lights and captured using a smartphone camera. The position of camera is determined using a novel Perspective-n-Point problem algorithm, which determines the geometric perspective of the LED coordinates. The experimental results show that the proposed system offers mean position errors of 4.81 and 6.58 cm for the heights of 50 cm and 80 cm, respectively.

Federated learning (FL) has the potential to empower Internet-of-Vehicles (IoV) networks by enabling smart vehicles (SVs) to participate in the learning process under the orchestration of a vehicular service provider while keeping data locally. In this paper, we propose a novel federated stochastic primal-dual algorithm with differential privacy (FedSPD-DP) to ensure robust privacy protection for FL based IoV (FL-IoV) systems. The FedSPD-DP algorithm leverages multiple steps of local stochastic gradient descent (SGD) and partial client participation (PCP) to improve communication efficiency while incorporating differential privacy (DP) to ensure privacy protection. Our theoretical analysis explores the impact of these strategies on learning performance. Specifically, we demonstrate that the data sampling strategy and PCP enhance data privacy, while a larger number of local SGD steps may increase privacy leakage, revealing a non-trivial tradeoff between communication efficiency and privacy protection. Extensive experiments on real-world data validate the effectiveness of the proposed algorithm, showing superior performance compared to state-of-the-art FL algorithms, and confirming the analytical results and properties.

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients’ private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients’ privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this paper, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets.

Federated learning (FL) has been recognized as a rapidly growing research area, where the model is trained over massively distributed clients under the orchestration of a parameter server (PS) without sharing clients’ data. This paper delves into a class of federated problems characterized by non-convex and non-smooth loss functions, that are prevalent in FL applications but challenging to handle due to their intricate non-convexity and non-smoothness nature and the conflicting requirements on communication efficiency and privacy protection. In this paper, we propose a novel federated primal-dual algorithm with bidirectional model sparsification tailored for non-convex and non-smooth FL problems, and differential privacy is applied for privacy guarantee. Its unique insightful properties and some privacy and convergence analyses are also presented as the FL algorithm design guidelines. Extensive experiments on real-world data are conducted to demonstrate the effectiveness of the proposed algorithm and much superior performance than some state-of-the-art FL algorithms, together with the validation of all the analytical results and properties.

Federated learning (FL) has emerged as a prominent approach for distributed machine learning, enabling collaborative model training while preserving data privacy. However, the presence of non-i.i.d. data and the need for robust privacy protection introduce significant challenges in theoretically analyzing the performance of FL algorithms. In this paper, we present novel theoretical analysis on typical differentially private federated averaging (DP-FedAvg) by judiciously considering the impact of non-i.i.d. data on convergence and privacy guarantees. Our contributions are threefold: (i) We introduce a theoretical framework for analyzing the convergence of DP-FedAvg algorithm by considering different client sampling and data sampling strategies, privacy amplification and non-i.i.d. data. (ii) We explore the privacy–utility tradeoff and demonstrate how client strategies interact with differential privacy to affect learning performance. (iii) We provide extensive experimental validation using real-world datasets to verify our theoretical findings.