A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

TL;DR: Zhang et al. as discussed by the authors proposed model-contrastive federated learning (MOON) to correct the local training of individual parties, i.e., conducting contrastive learning in model-level.

TL;DR: In this paper, a comprehensive survey of the emerging applications of federated learning in IoT networks is provided, which explores and analyzes the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing and IoT privacy and security.

TL;DR: In this article, a survey of federated learning (FL) topics and research fields is presented, including core system models and designs, application areas, privacy and security, and resource management.

TL;DR: A more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL is provided to enable data scientists to build better privacy-preserved solutions for industries in critical need of FL.

TL;DR: The first constructions for secure aggregation that achieve polylogarithmic communication and computation per client are presented and an application of secure aggregation to the task of secure shuffling is shown which enables the first cryptographically secure instantiation of the shuffle model of differential privacy.

TL;DR: In this paper, the authors investigate the feasibility of applying differential privacy techniques to protect the patient data in a federated learning setup and show that there is a trade-off between model performance and privacy protection costs.

Abstract: IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many networks already have vulnerable IoT devices that are easy to compromise. This has led to a new category of malware specifically targeting IoT devices. However, existing intrusion detection techniques are not effective in detecting compromised IoT devices given the massive scale of the problem in terms of the number of different types of devices and manufacturers involved. In this paper, we present DIoT, an autonomous self-learning distributed system for detecting compromised IoT devices. DIoT builds effectively on device-type-specific communication profiles without human intervention nor labeled data that are subsequently used to detect anomalous deviations in devices' communication behavior, potentially caused by malicious adversaries. DIoT utilizes a federated learning approach for aggregating behavior profiles efficiently. To the best of our knowledge, it is the first system to employ a federated learning approach to anomaly-detection-based intrusion detection. Consequently, DIoT can cope with emerging new and unknown attacks. We systematically and extensively evaluated more than 30 off-the-shelf IoT devices over a long term and show that DIoT is highly effective (95.6% detection rate) and fast (257 ms) at detecting devices compromised by, for instance, the infamous Mirai malware. DIoT reported no false alarms when evaluated in a real-world smart home deployment setting.

TL;DR: A new, general geo-distributed ML system, Gaia, is introduced that decouples the communication within a data center from the communication between data centers, enabling different communication and consistency models for each.