Systems and methods are provided for creating and sharing knowledge among design houses. In particular, examples of the presently disclosed technology leverage the concepts of meta-optimizing and collaborative learning to reduce the computational burden shouldered by individual design houses using inverse design techniques to find optimal designs in a manner which protects intellectual property sensitive information. Examples may share versions of a central meta-optimizer (i.e. local meta-optimizers) among design houses targeting different (but related) design tasks. A local meta-optimizer can be trained to indirectly optimize a design task by computing hyper-parameters for a design house's private optimization function. The private optimization function may be using inverse design techniques to find an optimal design for a design task. This may correspond to finding a global minimum of a cost function using gradient descent techniques or more advanced global optimization techniques.