ConferenceMobihoc 2025 Proceedings of the 2025 International Symposium on Theory Algorithmic Foundations and Protocol Design for Mobile Networks and Mobile Computing · October 23, 2025
Federated Unlearning (FU) enables the removal of specific clients' data influence from trained models. However, in non-IID settings, removing clients creates critical side effects: remaining clients with similar data distributions suffer disproportionate p ...
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ConferenceProceedings of the International Symposium on Modeling and Optimization in Mobile Ad Hoc and Wireless Networks Wiopt · January 1, 2025
The rapid growth of AI-generated content (AIGC) services has created an urgent need for effective prompt pricing strategies, yet current approaches overlook users’ strategic two-step decision-making process in selecting and utilizing generative AI models. ...
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ConferenceSensys 2024 Proceedings of the 2024 ACM Conference on Embedded Networked Sensor Systems · November 4, 2024
We present a privacy-preserving room occupancy estimation method using federated analytics of Bluetooth Low Energy (BLE) packets. By processing data locally and reporting only aggregated device counts, our approach preserves user privacy while achieving 95 ...
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ConferenceProceedings of the International Symposium on Mobile Ad Hoc Networking and Computing Mobihoc · October 1, 2024
In this demo, we introduce FedCampus, a privacy-preserving mobile application for smart campus with federated learning (FL) and federated analytics (FA). FedCampus enables cross-platform on-device FL/FA for both iOS and Android, supporting continuously mod ...
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ConferenceProceedings of the International Symposium on Mobile Ad Hoc Networking and Computing Mobihoc · October 1, 2024
A proper mechanism design can help federated learning (FL) to achieve good social welfare by coordinating self-interested clients through the learning process. However, existing mechanisms neglect the network effects of client participation, leading to sub ...
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ConferenceACM International Conference Proceeding Series · August 3, 2024
We present FedSM, a Federated Spectrum Management architecture to increase channel utilization (CU) and reduce latency, while protecting users' data privacy. We employ hedonic coalition formation game for spectrum allocation. Within each coalition, we desi ...
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ConferenceIEEE International Conference on Communications · January 1, 2024
Federated learning (FL) has recently received more and more attention in the joint field of distributed machine learning (ML) and privacy computing. Similar to the traditional ML systems, there exists the need of effective and efficient unlearning algorith ...
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ConferenceIEEE INFOCOM 2024 IEEE Conference on Computer Communications Workshops INFOCOM Wkshps 2024 · January 1, 2024
We present Fedkit, a federated learning (FL) system tailored for cross-platform FL research on Android and iOS devices. Fedkit pipelines cross-platform FL development by enabling model conversion, hardware-accelerated training, and cross-platform model agg ...
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ConferenceProceedings International Conference on Distributed Computing Systems · January 1, 2024
In federated learning (FL), distributed users collaboratively train a neural network model under the coordination of a central server. However, during the training process, clients often exhibit time-varying availability and have non-independent and non-id ...
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Conference2024 IEEE International Conference on E Health Networking Application and Services Healthcom 2024 · January 1, 2024
Deep learning-based techniques have been widely utilized for brain tumor segmentation using both single and multi-modal Magnetic Resonance Imaging (MRI) images. Most current studies focus on centralized training due to the intrinsic challenge of data shari ...
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ConferenceProceedings International Conference on Distributed Computing Systems · January 1, 2023
Incentive mechanism is crucial for federated learning (FL) when rational clients do not have the same interests in the global model as the server. However, due to system heterogeneity and limited budget, it is generally impractical for the server to incent ...
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ConferenceProceedings International Conference on Distributed Computing Systems · January 1, 2023
In this paper, we propose FedRos, a Federated Reinforcement Learning based multi-robot system, which enables networked robots collaboratively to train a shared model without sharing their private sensing data. Firstly, we present the FedRos pipeline that e ...
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