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Found 5 papers, 0 papers with code
Real-Time FJ/MAC PDE Solvers via Tensorized, Back-Propagation-Free Optical PINN Training
Despite the ultra-high speed of optical neural networks, training a PINN on an optical chip is hard due to (1) the large size of photonic devices, and (2) the lack of scalable optical memory devices to store the intermediate results of back-propagation (BP).
Tensor-Compressed Back-Propagation-Free Training for (Physics-Informed) Neural Networks
Backward propagation (BP) is widely used to compute the gradients in neural network training.
Knowledge-aided Federated Learning for Energy-limited Wireless Networks
Inspired by this, we define a new objective function, i. e., the weighted scheduled data sample volume, to transform the inexplicit global loss minimization problem into a tractable one for device scheduling, bandwidth allocation, and power control.
Dynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing
To this end, this work considers dynamic task software caching at the MEC server to assist users' task execution.
Efficient Wireless Federated Learning with Partial Model Aggregation
On this basis, we maximize the scheduled data size to minimize the global loss function through jointly optimize the device scheduling, bandwidth allocation, computation and communication time division policies with the assistance of Lyapunov optimization.
