Previous acoustic transfer methods rely on extensive precomputation and storage of data to enable real-time interaction and auditory feedback. However, these methods struggle with complex scenes, especially when dynamic changes in object position, material, and size significantly alter sound effects. These continuous variations lead to fluctuating acoustic transfer distributions, making it challenging to represent with basic data structures and render efficiently in real time. To address this challenge, we present Neural Acoustic Transfer, a novel approach that leverages implicit neural representations to encode acoustic transfer functions and their variations. This enables real-time prediction of dynamically evolving sound fields and their interactions with the environment under varying conditions. To efficiently generate high-quality training data for the neural acoustic field while avoiding reliance on mesh quality of a model, we develop a fast and efficient Monte-Carlo-based boundary element method (BEM) approximation, suitable for general scenarios with smooth Neumann boundary conditions. In addition, we devise strategies to mitigate potential singularities during the synthesis of training data, thereby enhancing its reliability. Together, these methods provide robust and accurate data that empower the neural network to effectively model complex sound radiation space. We demonstrate our method's numerical accuracy and runtime efficiency (within several milliseconds for 30 s audio) through comprehensive validation and comparisons in diverse acoustic transfer scenarios. Our approach allows for efficient and accurate modeling of sound behavior in dynamically changing environments, which can benefit a wide range of interactive applications such as virtual reality, augmented reality, and advanced audio production.

Federated learning is a popular distributed machine learning paradigm that enables collaborative model training at multiple entities via exchanging intermediate learning results. Security and communication efficiency are crucial for successful applications of federated learning in various privacy-sensitive services. However, existing work focused on gradient defense and communication efficiency separately, and also incurred additional computation, signaling, and accuracy overhead. A lightweight (in terms of time-complexity and signaling) technique that simultaneously achieves security and communication efficiency is critical for massive resource-constrained devices (e.g., Internet-of-Things generating the data), but has yet to be established. This paper proposes a secure and efficient federated learning framework with provable communication-accuracy-security performance guarantees. A low-complexity and signaling-free stochastic quantization module is added at the client side that quantizes the original local gradients to discrete values for communication-efficient global aggregation. The stochastic quantization module is shown to be interpreted as triangular or Gaussian-multiply-triangular noises under uniform or Gaussian distributions of local gradients, hence protecting data privacy. We prove that the proposed framework exhibits an {O(log21/δ),O(δ2),O(1/δ)}-tradeoff between the communication overhead, model accuracy, and data protection, where δ is an adjustable quantization interval. Experimental results validate the tradeoff and the superiority of the proposed stochastic quantization technique in terms of communication efficiency (only 14.1% of differential privacy and 0.2% of homomorphic encryption) and computation complexity (similar to differential privacy and only 0.03% of homomorphic encryption). Under the same data protection performance, the proposed approach also outperforms (in terms of accuracy) differential privacy in all the 9 comparison settings on CIFAR10 dataset.