![]() ![]() CS uses a complementary and collaborative pruning done at the server and the clients. Third, we cover Complement Sparsification (CS), an FL pruning mechanism that achieves low bidirectional communication overhead between the server and the clients, low computation overhead at the clients, and good model accuracy. FMLL uses federated learning to protect user privacy and reduce bandwidth consumption. The model fuses BiLSTM and CNN layers, where BiLSTM learns the speed and direction of the mobile users, and CNN learns information such as user movement preferences. The meta-location generation module represents the user location data as relative points in an abstract 2D space, which enables learning across different physical spaces. FMLL has three components: a meta-location generation module, a prediction model, and a federated learning framework. Second, we present Federated Meta-Location Learning (FMLL) on smart phones for fine-grained location prediction, based on GPS traces collected on the phones. The key idea of ZoneFL is to adapt FL models to user behaviors in different geographical zones. We also use FLSys in the design and implementation of ZoneFL, an FL system that divides the physical space into geographical zones mapped to a mobile-edge-cloud architecture for good model accuracy and scalability. FLSys adopts a modular design and is implemented in Android and AWS cloud. ![]() Furthermore, FLSys provides advanced privacy preserving mechanisms and a common API for third-party app developers to access FL models. In FLSys, different DL models with different FL aggregation methods can be trained and accessed concurrently by different apps. This talk will present an overview of our work in this area, focusing on FL systems, applications, and optimizations.įirst, we describe FLSys, a mobile-cloud FL system that balances model performance with resource consumption, tolerates communication failures, and achieves scalability. Cristian Borcea ( NJIT) titled:" Federated Learning for Mobile and IoT Devices" Everyone interested is cordially invited to attend! Title:įederated Learning for Mobile and IoT Devicesįederated Learning (FL) has emerged as a new distributed machine learning paradigm that enables privacy-aware training and inference on mobile and IoT devices with help from the cloud. We are pleased to inform you about the upcoming seminar by Prof. Talk on "Federated Learning for Mobile and IoT Devices" by Prof. ![]()
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