Federated learning emerges as a solution to the dilemma of data silos while safeguarding data privacy, particularly relevant in the consumer electronics sector where user data privacy is paramount. However, federated learning is generally employed in a heterogeneous scenario, consisting of various factors that influence the training efficiency and accuracy of the federated learning models. There are many classic references focusing on federated communications, federated robustness and federated fairness, conversely, few of them clarify and summary systematically the influence of heterogeneity on the effect of federated learning. Therefore, we provide an overview of three heterogeneous challenges faced by federated learning in practical applications: device heterogeneity, data heterogeneity and model heterogeneity, and analyze their influence on federated learning. This is especially crucial in consumer electronics, where heterogeneity directly influence the performance and user experience of AI-driven features. And then, we highlight current solutions, ideas and challenges to compare different strategies for facing heterogeneous problems and outline several directions of future work that are relevant to a wide range of research communities.

Consensus-based Speed Advisory System (CSAS) is used to recommend a consensus speed to a group of vehicles for specific application purposes, such as minimizing emissions or energy consumption. To remedy data privacy concerns for speed advisory services, the latest works have investigated how to get an optimal speed in a privacy-preserving manner. However, almost all the designs are based on a centralized architecture, which could still meet service trust issues, such as that a random speed could be recommended when the central server meets cyber incursion attacks. To address the problem, in this paper we propose BSAS, a trustworthy and privacy-preserving CSAS over the blockchain. Specifically, BSAS follows a fully decentralized architecture with cryptographic primitives to guarantee service trust and data privacy. Moreover, to encourage vehicles to participate in the service computing process, a value-driven incentive mechanism is also employed. We present the detailed design and implementation of BSAS, and our emulation results show that the proposed BSAS can achieve promising system performance in terms of real-time speed recommendation in a trustworthy and privacy-preserving way.

Digital Twins (DTs) can generate digital replicas for mobile networks (MNs) that accurately reflect the state of MN. Machine learning (ML) models trained in DT for MN (DTMN) virtual environments can be more robustly implemented in MN. This can avoid the training difficulties and runtime errors caused by MN instability and multiple failures. However, when using data from various devices in the MN system, DTs must prioritize data privacy. Federated learning (FL) enables the construction of models without data leaving devices to protect DTMN data privacy. Nevertheless, FL’s privacy protection needs further improvement for it only guarantees device-level data ownership but ignores that models may retain private information from data. Therefore, this paper focuses on data forgetting in privacy protection, and proposes a novel FL-based unlearning framework (FedME ² ), which contains MEval and MErase modules. Guided by memory evaluation information from MEval and employing MErase’s multi-loss training approach, FedME ² gets accurate data forgetting in DTMN. In four DTMN virtual environments, FedME ² achieves an average data forgetting rate of approximately 75% for global models under FL and kept the influence on global models’ accuracy below 4%. FedME ² has better data forgetting and improves DTMN data privacy protection while guaranteeing model accuracy.

Now public transportation, included bus and subway occupies a greater role in city transport, and the layout of traffic stations is the most important part of planning and design. However, some unpredicted factors for construction of traffic stations results in a low utilization rate of public transportation resources, for example, the layout of bus stops is chaotic, there is no clear layout scope, and there is a lack of integration with residents’ travel hotspots. Towards these challenges modern transport faces, we firstly analyze the distribution of bus stops and subway stations to determine the area range needs to be optimized in the traffic net from the perspective of time and space. And then, we propose an optimization method, called ’partial area clustering’ (PAC), to improve the utilization by changing and renewing the original distribution. The novel method was based on the K-means algorithm in the field of machine learning. PAC worked to search the suitable bus platforms as the center and modified the original one to the subway. Experiment has shown that the use of public transport resources has increased by 20%. The study uses a similar cluster algorithm to solve transport networks’ problems in a novel but practical term. As a result, the PAC is expected to be used extensively in the transportation system construction process.