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1. Mitigating Data Poisoning Attacks On a Federated Learning-Edge Computing Network

   https://doi.org/10.1109/CCNC49032.2021.9369581  

   Doku, Ronald; Rawat, Danda B. (January 2021, 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC))

   null (Ed.)

   Edge Computing (EC) has seen a continuous rise in its popularity as it provides a solution to the latency and communication issues associated with edge devices transferring data to remote servers. EC achieves this by bringing the cloud closer to edge devices. Even though EC does an excellent job of solving the latency and communication issues, it does not solve the privacy issues associated with users transferring personal data to the nearby edge server. Federated Learning (FL) is an approach that was introduced to solve the privacy issues associated with data transfers to distant servers. FL attempts to resolve this issue by bringing the code to the data, which goes against the traditional way of sending the data to remote servers. In FL, the data stays on the source device, and a Machine Learning (ML) model used to train the local data is brought to the end device instead. End devices train the ML model using local data and then send the model updates back to the server for aggregation. However, this process of asking random devices to train a model using its local data has potential risks such as a participant poisoning the model using malicious data for training to produce bogus parameters. In this paper, an approach to mitigate data poisoning attacks in a federated learning setting is investigated. The application of the approach is highlighted, and the practical and secure nature of this approach is illustrated as well using numerical results. 

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2. Federated Optimization of ℓ0-norm Regularized Sparse Learning

   https://doi.org/10.3390/a15090319  

   Tong, Qianqian; Liang, Guannan; Ding, Jiahao; Zhu, Tan; Pan, Miao; Bi, Jinbo (September 2022, Algorithms)

   Regularized sparse learning with the ℓ0-norm is important in many areas, including statistical learning and signal processing. Iterative hard thresholding (IHT) methods are the state-of-the-art for nonconvex-constrained sparse learning due to their capability of recovering true support and scalability with large datasets. The current theoretical analysis of IHT assumes the use of centralized IID data. In realistic large-scale scenarios, however, data are distributed, seldom IID, and private to edge computing devices at the local level. Consequently, it is required to study the property of IHT in a federated environment, where local devices update the sparse model individually and communicate with a central server for aggregation infrequently without sharing local data. In this paper, we propose the first group of federated IHT methods: Federated Hard Thresholding (Fed-HT) and Federated Iterative Hard Thresholding (FedIter-HT) with theoretical guarantees. We prove that both algorithms have a linear convergence rate and guarantee for recovering the optimal sparse estimator, which is comparable to classic IHT methods, but with decentralized, non-IID, and unbalanced data. Empirical results demonstrate that the Fed-HT and FedIter-HT outperform their competitor—a distributed IHT, in terms of reducing objective values with fewer communication rounds and bandwidth requirements. 

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3. Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data

   https://doi.org/10.2196/23728  

   Liu, Jessica Chia; Goetz, Jack; Sen, Srijan; Tewari, Ambuj (January 2021, JMIR mHealth and uHealth)

   null (Ed.)

   Background The use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user’s data on that user’s device. By keeping data on users’ devices, federated learning protects users’ private data from data leaks and breaches on the researcher’s central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain. Objective We review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models. Methods We applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject. Results In a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average. Conclusions Our findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods. 

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4. ProvIoT: Detecting Stealthy Attacks in IoT through Federated Edge-Cloud Security

   Mukherjee, Kunal; Wiedemeier, Joshua; Wang, Qi; Kamimura, Junpei; Rhee, John Junghwan; Wei, James; Li, Zhichun; Yu, Xiao; Tang, Lu-An; Gui, Jiaping; et al (March 2024, ACM)

   Internet of Things (IoT) devices have increased drastically in complexity and prevalence within the last decade. Alongside the proliferation of IoT devices and applications, attacks targeting them have gained popularity. Recent large-scale attacks such as Mirai and VPNFilter highlight the lack of comprehensive defenses for IoT devices. Existing security solutions are inadequate against skilled adversaries with sophisticated and stealthy attacks against IoT devices. Powerful provenance-based intrusion detection systems have been successfully deployed in resource-rich servers and desktops to identify advanced stealthy attacks. However, IoT devices lack the memory, storage, and computing resources to directly apply these provenance analysis techniques on the device. This paper presents ProvIoT, a novel federated edge-cloud security framework that enables on-device syscall-level behavioral anomaly detection in IoT devices. ProvIoT applies federated learning techniques to overcome data and privacy limitations while minimizing network overhead. Infrequent on-device training of the local model requires less than 10% CPU overhead; syncing with the global models requires sending and receiving 2MB over the network. During normal offline operation, ProvIoT periodically incurs less than 10% CPU overhead and less than 65MB memory usage for data summarization and anomaly detection. Our evaluation shows that ProvIoT detects fileless malware and stealthy APT attacks with an average F1 score of 0.97 in heterogeneous real-world IoT applications. ProvIoT is a step towards extending provenance analysis to resource-constrained IoT devices, beginning with well-resourced IoT devices such as the RaspberryPi, Jetson Nano, and Google TPU. 

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5. Memory-adaptive Depth-wise Heterogenous Federated Learning

   Zhang, Kai; Dai, Yutong; Wang, Hongyi; Xing, Eric; Chen, Xun; Sun, Lichao Sun. (May 2023, Federated Learning Systems (FLSys) Workshop @ MLSys 2023)

   Federated learning is a promising paradigm that allows multiple clients to collaboratively train a model without sharing the local data. However, the presence of heterogeneous devices in federated learning, such as mobile phones and IoT devices with varying memory capabilities, would limit the scale and hence the performance of the model could be trained. The mainstream approaches to address memory limitations focus on width-slimming techniques, where different clients train subnetworks with reduced widths locally and then the server aggregates the subnetworks. The global model produced from these methods suffers from performance degradation due to the negative impact of the actions taken to handle the varying subnetwork widths in the aggregation phase. In this paper, we introduce a memory-adaptive depth-wise learning solution in FL called FEDEPTH, which adaptively decomposes the full model into blocks according to the memory budgets of each client and trains blocks sequentially to obtain a full inference model. Our method outperforms state-of-the-art approaches, achieving 5% and more than 10% improvements in top-1 accuracy on CIFAR-10 and CIFAR-100, respectively. We also demonstrate the effectiveness of depth-wise fine-tuning on ViT. Our findings highlight the importance of memory-aware techniques for federated learning with heterogeneous devices and the success of depth-wise training strategy in improving the global model’s performance. 

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