Federated learning provides a promising privacy-preserving way for utilizing large-scale private edge data from massive Internet-of-Things (IoT) devices. While existing research extensively studied optimizing the learning process, computing efficiency, and communication overhead, one important and often overlooked aspect is that participants contribute predictive knowledge from their data, impacting the quality of the federated models learned. While FedAvg treats each client equally and assigns weight solely based on the number of samples, the diversity of samples on each client could greatly affect the local update performance and the final aggregated model. In this paper, we propose a novel approach to address this issue by introducing a Weighted Averaging (WeiAvg) framework that emphasizes updates from high-diversity clients and diminishes the influence of those from low-diversity clients. Specifically, we introduced a projection-based approximation method to estimate the diversity of client data, instead of the computation of an entropy. We use the approximation because the locally computed entropy may not be transmitted due to excess privacy risk. Extensive experimental results show that WeiAvg converges faster and achieves higher accuracy than the original FedAvg algorithm and FedProx.
The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of scams of \$102m to financial institutions in 2022. Developing a mechanism for battling financial crimes is an impending task that requires in-depth collaboration from multiple institutions, and yet such collaboration imposed significant technical challenges due to the privacy and security requirements of distributed financial data. For example, consider the Society for Worldwide Interbank Financial Telecommunications (SWIFT) system, which generates 42 million transactions per day across its 11,000 global institutions. Training a detection model of fraudulent transactions requires not only secured SWIFT transactions but also the private account activities of those involved in each transaction from corresponding bank systems. The distributed nature of both samples and features prevents most existing learning systems from being directly adopted to handle the data mining task. In this paper, we collectively address these challenges by proposing a hybrid federated learning system that offers secure and privacy-aware learning and inference for financial crime detection. We conduct extensive empirical studies to evaluate the proposed framework's detection performance and privacy-protection capability, evaluating its robustness against common malicious attacks of collaborative learning. We release our source code at https://github.com/illidanlab/HyFL .
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Federated learning (FL) is a distributed and privacy-preserving learning framework for predictive modeling with massive data generated at the edge by Internet of Things (IoT) devices. One major challenge preventing the wide adoption of FL in IoT is the pervasive power supply constraints of IoT devices due to the intensive energy consumption of battery-powered clients for local training and model updates. Low battery levels of clients eventually lead to their early dropouts from edge networks, loss of training data jeopardizing the performance of FL, and their availability to perform other designated tasks. In this paper, we propose FedLE, an energy-efficient client selection framework that enables lifespan extension of edge IoT networks. In FedLE, the clients first run for a minimum epoch to generate their local model update. The models are partially uploaded to the server for calculating similarities between each pair of clients. Clustering is performed against these client pairs to identify those with similar model distributions. In each round, low-powered clients have a lower probability of being selected, delaying the draining of their batteries. Empirical studies show that FedLE outperforms baselines on benchmark datasets and lasts more training rounds than FedAvg with battery power constraints.
* 6 pages, 6 figures, accepted to 2023 IEEE International Conference on
Communications (ICC 2023)
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge. With its simple yet effective approach, federated learning (FL) is proved to be a natural solution for massive user-owned devices in edge computing with distributed and private training data. Most vanilla FL algorithms based on FedAvg follow a naive star topology, ignoring the heterogeneity and hierarchy of the volatile edge computing architectures and topologies in reality. In this paper, we conduct a comprehensive survey on the existing work of optimized FL models, frameworks, and algorithms with a focus on their network topologies. After a brief recap of FL and edge computing networks, we introduce various types of edge network topologies, along with the optimizations under the aforementioned network topologies. Lastly, we discuss the remaining challenges and future works for applying FL in topology-specific edge networks.
Recently, a new distributed learning scheme called Federated Learning (FL) has been introduced. FL is designed so that server never collects user-owned data meaning it is great at preserving privacy. FL's process starts with the server sending a model to clients, then the clients train that model using their data and send the updated model back to the server. Afterward, the server aggregates all the updates and modifies the global model. This process is repeated until the model converges. This study focuses on an FL setting called cross-device FL, which trains based on a large number of clients. Since many devices may be unavailable in cross-device FL, and communication between the server and all clients is extremely costly, only a fraction of clients gets selected for training at each round. In vanilla FL, clients are selected randomly, which results in an acceptable accuracy but is not ideal from the overall training time perspective, since some clients are slow and can cause some training rounds to be slow. If only fast clients get selected the learning would speed up, but it will be biased toward only the fast clients' data, and the accuracy degrades. Consequently, new client selection techniques have been proposed to improve the training time by considering individual clients' resources and speed. This paper introduces the first availability-aware selection strategy called MDA. The results show that our approach makes learning faster than vanilla FL by up to 6.5%. Moreover, we show that resource heterogeneity-aware techniques are effective but can become even better when combined with our approach, making it faster than the state-of-the-art selectors by up to 16%. Lastly, our approach selects more unique clients for training compared to client selectors that only select fast clients, which reduces our technique's bias.