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Smart city article details
| Title | Federated Learning For Urban Sensing Systems: A Comprehensive Survey On Attacks, Defences, Incentive Mechanisms, And Applications |
|---|---|
| ID_Doc | 26353 |
| Authors | Kapoor A.; Kumar D. |
| Year | 2025 |
| Published | IEEE Communications Surveys and Tutorials, 27, 2 |
| DOI | http://dx.doi.org/10.1109/COMST.2024.3434510 |
| Abstract | In recent years, advancements in Artificial Intelligence (AI), the Internet of Things (IoT) and wireless technologies have propelled the evolution of smart cities. Urban sensing systems collect real-time data from urban areas for various applications, such as environmental monitoring, healthcare, and intelligent transportation, that contribute to the growth of smart cities. In urban sensing, the active participation of users gives rise to participatory sensing, where individuals contribute real-time data through their smartphones or IoT devices, but it encounters bottlenecks in communication, network latency, and user privacy with an exponential rise in data. A prominent characteristic of urban sensing applications is the highly individualized and personal nature of the data, e.g., location and time. Hence, adequate privacy and security provisions are required for these applications to succeed on a high scale. Conventional centralised machine learning approaches expose participants to potential vulnerabilities from malicious tasking servers or inference based on anonymized data. Federated learning (FL) has been proposed as the most viable alternative that leverages the advances in modern-day smartphones’ computation and communication capabilities by allowing participants to train local models on their devices. These models are aggregated by the application server to form a global model without the need for users to share their private data. However, large-scale FL-based urban sensing systems are still not practical due to various challenges associated with their real-life implementation. This paper presents a comprehensive survey addressing practical challenges in implementing FL-based urban sensing applications, e.g., inference attacks, poisoning attacks, and fair incentivization to participants while preserving privacy. We then provide an extensive survey on the use of FL in various urban sensing applications, highlighting that current applications do not simultaneously address all three aforementioned challenges. We conclude this survey by highlighting the research challenges to form a practical FL-based urban sensing system and future research directions. © 1998-2012 IEEE. |
| Author Keywords | Federated learning; incentive mechanism; inference attack; poisoning attack; smart city; urban sensing |
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