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1. Integrated Distributed Wireless Sensing with Over-The-Air Federated Learning

   https://doi.org/10.1109/IGARSS52108.2023.10282842  

   Gao, Shijian; Yan, Jia; Giannakis, Georgios B. (July 2023, IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium)

   Over-the-air federated learning (OTA-FL) is a communication-effective approach for achieving distributed learning tasks. In this paper, we aim to enhance OTA-FL by seamlessly combining sensing into the communication-computation integrated system. Our research reveals that the wireless waveform used to convey OTA-FL parameters possesses inherent properties that make it well-suited for sensing, thanks to its remarkable auto-correlation characteristics. By leveraging the OTA-FL learning statistics, i.e., means and variances of local gradients in each training round, the sensing results can be embedded therein without the need for additional time or frequency resources. Finally, by considering the imperfections of learning statistics that are neglected in the prior works, we end up with an optimized the transceiver design to maximize the OTA-FL performance. Simulations validate that the proposed method not only achieves outstanding sensing performance but also significantly lowers the learning error bound. 

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2. Integrated Distributed Wireless Sensing with Over-the-Air Federated Learning

   Gao, Shijian (July 2023, IEEE International Geoscience and Remote Sensing Symposium proceedings)

   Over-the-air federated learning (OTA-FL) is a communication effective approach for achieving distributed learning tasks. In this paper, we aim to enhance OTA-FL by seamlessly combining sensing into the communication-computation integrated system. Our research reveals that the wireless waveform used to convey OTA-FL parameters possesses inherent properties that make it well-suited for sensing, thanks to its remarkable auto- correlation characteristics. By leveraging the OTA-FL learning statistics, i.e., means and variances of local gradients in each training round, the sensing results can be embedded therein without the need for additional time or frequency resources. Finally, by considering the imperfections of learning statistics that are neglected in the prior works, we end up with an optimized the transceiver design to maximize the OTA-FL performance. Simulations validate that the proposed method not only achieves outstanding sensing performance but also significantly lowers the learning error bound. 

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3. Adaptive Federated Learning for Automatic Modulation Classification Under Class and Noise Imbalance

   https://doi.org/10.1609/aaaiss.v3i1.31223  

   Sanchez_Viloria, Jose Angel; Stripelis, Dimitris; Markopoulos, Panos P; Sklivanitis, George; Pados, Dimitris A (May 2024, Proceedings of the AAAI Symposium Series)

   The ability to rapidly understand and label the radio spectrum in an autonomous way is key for monitoring spectrum interference, spectrum utilization efficiency, protecting passive users, monitoring and enforcing compliance with regulations, detecting faulty radios, dynamic spectrum access, opportunistic mesh networking, and numerous NextG regulatory and defense applications. We consider the problem of automatic modulation classification (AMC) by a distributed network of wireless sensors that monitor the spectrum for signal transmissions of interest over a large deployment area. Each sensor receives signals under a specific channel condition depending on its location and trains an individual model of a deep neural network (DNN) accordingly to classify signals. To improve modulation classification accuracy, we consider federated learning (FL) where each individual sensor shares its trained model with a centralized controller, which, after aggregation, initializes its model for the next round of training. Without exchanging any spectrum data (such as in cooperative spectrum sensing), this process is repeated over time. A common DNN is built across the net- work while preserving the privacy associated with signals collected at different locations. Given their distributed nature, the statistics of the data across these sensors are likely to differ significantly. We propose the use of adaptive federated learning for AMC. Specifically, we use FEDADAM -an algorithm using Adam for server optimization – and ex- amine how it compares to the FEDAVG algorithm -one of the standard FL algorithms, which averages client parameters after some local iterations, in particular in challenging scenarios that include class imbalance and/or noise-level imbalance across the network. Our extensive numerical studies over 11 standard modulation classes corroborate the merit of adaptive FL, outperforming its standard alternatives in various challenging cases and for various network sizes. 

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4. Distributed Architecture Search Over Heterogeneous Distributions

   Mushtaq, Erum; He, Chaoyang; Ding, Jie; Avestimehr, Salman (November 2023, Transactions on machine learning research)

   Federated learning (FL) is an efficient learning framework that assists distributed machine learning when data cannot be shared with a centralized server. Recent advancements in FL use predefined architecture-based learning for all clients. However, given that clients’ data are invisible to the server and data distributions are non-identical across clients, a predefined architecture discovered in a centralized setting may not be an optimal solution for all the clients in FL. Motivated by this challenge, we introduce SPIDER, an algorithmic frame- work that aims to Search PersonalIzed neural architecture for feDERated learning. SPIDER is designed based on two unique features: (1) alternately optimizing one architecture- homogeneous global model in a generic FL manner and architecture-heterogeneous local models that are connected to the global model by weight-sharing-based regularization, (2) achieving architecture-heterogeneous local models by a perturbation-based neural architecture search method. Experimental results demonstrate superior prediction performance compared with other state-of-the-art personalization methods. Code is available at https://github.com/ErumMushtaq/SPIDER.git. 

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5. Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems

   Bo Sun, Russell Lee (January 2021, Advances in Neural Information Processing Systems 34 (NeurIPS 2021))

   This paper leverages machine-learned predictions to design competitive algorithms for online conversion problems with the goal of improving the competitive ratio when predictions are accurate (i.e., consistency), while also guaranteeing a worst-case competitive ratio regardless of the prediction quality (i.e., robustness). We unify the algorithmic design of both integral and fractional conversion problems, which are also known as the 1-max-search and one-way trading problems, into a class of online threshold-based algorithms (OTA). By incorporating predictions into design of OTA, we achieve the Pareto-optimal trade-off of consistency and robustness, i.e., no online algorithm can achieve a better consistency guarantee given for a robustness guarantee. We demonstrate the performance of OTA using numerical experiments on Bitcoin conversion. 

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