Patents by Inventor David Froelich

David Froelich has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • System and method for privacy-preserving distributed training of machine learning models on distributed datasets
    Patent number: 12206758
    Abstract: A system for privacy-preserving distributed training of a global model on distributed datasets has a plurality of data providers being communicatively coupled. Each data provider has a local model and a local training dataset for training the local model using an iterative training algorithm. Further it has a portion of a cryptographic distributed secret key and a corresponding collective cryptographic public key of a multiparty fully homomorphic encryption scheme. All models are encrypted with the collective public key. Each data provider trains its local model using the respective local training dataset, and combines the local model with the current global model into a current local model. A data provider homomorphically combines current local models into a combined model, and updates the current global model based on the combined model. The updated global model is provided to at least a subset of the other data providers.
    Type: Grant
    Filed: May 8, 2020
    Date of Patent: January 21, 2025
    Assignee: Ecole Polytechnique Federale De Lausanne (EPFL)
    Inventors: David Froelicher, Juan Ramon Troncoso-Pastoriza, Apostolos Pyrgelis, Sinem Sav, Joao Gomes De Sa E Sousa, Jean-Pierre Hubaux, Jean-Philippe Bossuat
  • SYSTEM AND METHOD FOR PRIVACY-PRESERVING DISTRIBUTED TRAINING OF NEURAL NETWORK MODELS ON DISTRIBUTED DATASETS
    Publication number: 20230325529
    Abstract: A computer-implemented method and a distributed computer system (100) for privacy-preserving distributed training of a global neural network model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being communicatively coupled. Each data provider has a respective local training dataset (DS1 to DSn) and a vector of output labels (OL1 to OLn) for training the global model. Further, it has a portion of a cryptographic distributed secret key (SK1 to SKn) and a corresponding collective cryptographic public key (CPK) of a multiparty fully homomorphic encryption scheme, with the weights of the global model being encrypted with the collective public key. Each data provider (DP1) computes and aggregates, for each layer of the global model, encrypted local gradients (LG1) using the respective local training dataset (DS1) and output labels (OL1), with forward pass and backpropagation using stochastic gradient descent.
    Type: Application
    Filed: August 27, 2020
    Publication date: October 12, 2023
    Inventors: Sinem Sav, Juan Ramon Troncoso-Pastoriza, Apostolos Pyrgelis, David Froelicher, Joao Gomes De Sa E Sousa, Jean-Philippe Bossuat, Jean-Pierre Hubaux
  • SYSTEM AND METHOD FOR PRIVACY-PRESERVING DISTRIBUTED TRAINING OF MACHINE LEARNING MODELS ON DISTRIBUTED DATASETS
    Publication number: 20230188319
    Abstract: A computer-implemented method and a distributed computer system (100) for privacy-preserving distributed training of a global model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being communicatively coupled. Each data provider has a respective local model (LM1 to LMn) and a respective local training dataset (DS1 to DSn) for training the local model using an iterative training algorithm (IA). Further it has a portion of a cryptographic distributed secret key (SK1 to SKn) and a corresponding collective cryptographic public key (CPK) of a multiparty fully homomorphic encryption scheme, with the local and global model being encrypted with the collective public key. Each data provider (DP1) trains its local model (LM1) using the respective local training dataset (DS1) by executing gradient descent updates of its local model (LM1), and combining (1340) the updated local model (LM1?) with the current global model (GM) into a current local model (LM1c).
    Type: Application
    Filed: May 8, 2020
    Publication date: June 15, 2023
    Inventors: David Froelicher, Juan Ramon Troncoso-Pastoriza, Apostolos Pyrgelis, Sinem Sav, Joao Gomes De Sa E Sousa, Jean-Pierre Hubaux, Jean-Philippe Bossuat
  • Integrated renewable energy and asset system
    Patent number: 9903618
    Abstract: An integrated renewable energy and asset system is provided.
    Type: Grant
    Filed: October 30, 2014
    Date of Patent: February 27, 2018
    Assignee: SunPower Corporation
    Inventors: Laurence Mackler, Brian Cuff, Hikaru Iwasaka, Alexander Keller, David Froelich
  • INTEGRATED RENEWABLE ENERGY AND ASSET SYSTEM
    Publication number: 20150113987
    Abstract: An integrated renewable energy and asset system is provided.
    Type: Application
    Filed: October 30, 2014
    Publication date: April 30, 2015
    Inventors: Laurence Mackler, Brian Cuff, Hikaru Iwasaka, Alexander Keller, David Froelich
  • Solar power generation assembly
    Patent number: D751976
    Type: Grant
    Filed: August 5, 2013
    Date of Patent: March 22, 2016
    Assignee: SunPower Corporation
    Inventors: Laurence Mackler, Brian Cuff, Hikaru Iwasaka, David Froelich
  • Solar power generation assembly
    Patent number: D825448
    Type: Grant
    Filed: February 4, 2016
    Date of Patent: August 14, 2018
    Assignee: SUNPOWER CORPORATION
    Inventors: Laurence Mackler, Brian Cuff, Hikaru Iwasaka, David Froelich