Abstract: Decentralized bilevel optimization techniques for personalized learning over a heterogenous network are provided. In one aspect, a decentralized learning system includes: a distributed machine learning network with multiple nodes, and datasets associated with the nodes; and a bilevel learning structure at each of the nodes for optimizing one or more features from each of the datasets using a decentralized bilevel optimization solver, while maintaining distinct features from each of the datasets. A method for decentralized learning is also provided.

Abstract: Various techniques for synchronizing illumination with an audio signal are disclosed. According to one aspect of the techniques, an audio signal continuously is received and processed one phase or period at a time in an analyzing or detecting unit. From one phase of the audio signal, a set of light controlling parameters are determined from a look-up-table. These light controlling parameters are used to adjust duty cycles of square waves used to drive a plurality of light sources such that the illumination produced is in accordance with the audio signal.

Abstract: Various techniques for synchronizing illumination with an audio signal are disclosed. According to one aspect of the techniques, an audio signal continuously is received and processed one phase or period at a time in an analyzing or detecting unit. From one phase of the audio signal, a set of light controlling parameters are determined from a look-up-table. These light controlling parameters are used to adjust duty cycles of square waves used to drive a plurality of light sources such that the illumination produced is in accordance with the audio signal.