Noise-robust federated learning via optimal transport

A method for aggregating a decentralized machine learning (ML) model in a resilient fashion in the presence of highly noisy data, the method comprising:

• a) creating local representations of each class on each edge device by computing a local Wasserstein barycenter (WB) for each class on each edge device, the local WB being a minimizer of a function over multiple distributions;
• b) collecting the local representations in a central server;
• c) combining the local representations at the central server to generate global representations;
• d) distributing the global representations from the central server to each edge device;
• and
• e) performing classification by computing a Wasserstein distance between a class representative and each respective data sample and choosing respective class labels based on a smallest Wasserstein distance for each class, thereby aggregating the decentralized ML model to give an aggregated decentralized ML model;
• and
• f) detecting anomalies, utilizing the aggregate decentralized ML model, in at least one of a cybersecurity setting and client behavior in a decentralized setting;
• and
• g) blocking future traffic from any sources of anomalies detected in step f), thereby improving security of a computing device on which the method is performed.

A system for aggregating a decentralized machine learning (ML) model in a resilient fashion in the presence of highly noisy data, the system comprising:

• a computing device;
• a processor disposed in the computing device;
• and
• a machine-readable medium disposed in the computing device, in operable communication with the processor, and having instructions stored thereon that, when executed by the processor, perform the following steps:
• a) creating local representations of each class on each edge device by computing a local Wasserstein barycenter (WB) for each class on each edge device, the local WB being a minimizer of a function over multiple distributions;
• b) collecting the local representations in a central server;
• c) combining the local representations at the central server to generate global representations;
• d) distributing the global representations from the central server to each edge device;
• e) performing classification by computing a Wasserstein distance between a class representative and each respective data sample and choosing respective class labels based on a smallest Wasserstein distance for each class, thereby aggregating the decentralized ML model to give an aggregated decentralized ML model;
• f) using the global representations to train local models;
• g) comparing the trained local models to global models;
• and
• h) detecting anomalies utilizing the aggregate decentralized ML model, in at least one of a cybersecurity setting and client behavior in a decentralized setting;
• i) blocking future traffic from any sources of anomalies detected in step h), thereby improving security of the computing device;
• and
• j) performing at least one of the following:
• j-1) controlling a power system to maintain voltage output in presence of highly noisy weather-related data, the decentralized ML model being used to maintain the voltage output of the power system, and steps a)-e) aggregating the decentralized ML model in the presence of the highly noisy weather-related data;
• j-2) controlling automated audio analysis in presence of environmental noise while preserving privacy of users, the decentralized ML model being used for the automated audio analysis, and steps a)-e) aggregating the decentralized ML model in the presence of the environmental noise;
• j-3) controlling image recognition in presence of highly noisy data, the decentralized ML model being used for the image recognition, and steps a)-e) aggregating the decentralized ML model in the presence of the highly noisy data;
• j-4) controlling an automated fire-fighter robot to operate normally in a highly noisy environment, the decentralized ML model being used to automate the fire-fighter robot, and steps a)-e) aggregating the decentralized ML model in the presence of the highly noisy environment;
• and
• j-5) controlling a rescue robot to explore highly noisy images and videos of an affected disaster area and make a rescue decision, the decentralized ML model being used to explore with the rescue robot, and steps a)-e) aggregating the decentralized ML model in the presence of the highly noisy images and videos,
• the combining of the local representations to generate the global representations comprising computing a respective global WB of the local WB of each class,
• the distributing of the global representations from the central server to each edge device comprising distributing the respective global WB of each class,
• steps a)-g) being performed as an optimal transport (OT)-based aggregation,
• the decentralized ML model being a federated learning (FL) model,
• the creating of the local representations comprising computing local moments,
• the performing of steps a)-g) being nonparametric, and
• the performing of steps a)-e) improving the computing device by freeing up memory and processor usage on the computing device via decreased efficiency loss on the computing device.