Federated Learning

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Google Research Moves Federated Learning Into TEEs: Gboard Now Trains With Externally Verifiable Differential Privacy

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Google Research has announced a next-generation Federated Learning (FL) system built on Trusted Execution Environments (TEEs). The research team claims externally verifiable central differential privacy (DP) guarantees for FL for the first time. What Problem Does TEE-Based Federated Learning Solve? Google introduced Federated Learning FL in 2017. It powers next-word prediction and Smart Compose on

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Step by Step Guide to Build and Compare FedAvg and FedProx Federated Learning on Non-IID CIFAR-10 with NVIDIA FLARE

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In this tutorial, we build an advanced federated learning experiment with NVIDIA FLARE. We compare FedAvg and FedProx on a non-IID CIFAR-10 setup, where client data is split using a Dirichlet distribution to simulate realistic label imbalance across federated sites. We use the NVFlare Job API to define and launch federated jobs, while the Client

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How to Build a Privacy-Preserving Federated Pipeline to Fine-Tune Large Language Models with LoRA Using Flower and PEFT

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In this tutorial, we demonstrate how to federate fine-tuning of a large language model using LoRA without ever centralizing private text data. We simulate multiple organizations as virtual clients and show how each client adapts a shared base model locally while exchanging only lightweight LoRA adapter parameters. By combining Flower’s federated learning simulation engine with

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