# Federated learning Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/federated-learning
> Markdown URL: https://aitinkerers.org/technologies/federated-learning.md
> Technology record last updated: 2026-04-12T17:32:55Z
> Generated: 2026-09-21T01:34:50Z

Federated Learning (FL) trains a shared AI model across numerous decentralized devices, keeping all raw training data local to ensure privacy and data sovereignty.

FL is a distributed machine learning paradigm that decouples model training from centralized data collection. The process is iterative: a central server sends the current global model to a subset of clients (e.g., smartphones, hospital servers). Each client trains the model locally on its private data, then sends only the model updates (weights or gradients) back to the server, never the raw data. The server aggregates these updates, typically using the Federated Averaging (FedAvg) algorithm, to create an improved global model. This approach directly addresses critical regulatory concerns like GDPR and HIPAA, enabling collaborative AI development in sensitive sectors like healthcare and powering on-device features, such as Google's Gboard predictive text.

- Official technology site: https://ai.google/research/federated-learning/
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Time-Bound AI Model](https://austin.aitinkerers.org/talks/rsvp_W9daxK1z0QQ)

AGI is the focus.

- Event context: August Meetup: Community AI Demos — 2024-08-08 — Austin
- Public talk page: https://austin.aitinkerers.org/talks/rsvp_W9daxK1z0QQ

### [Federated Learning with Flame](https://seattle.aitinkerers.org/talks/rsvp_HniMKJNpKf8)

Traditional machine learning depends on the centralization of data, but that comes with privacy and computational concerns. A reality with billions of edge devices diminish those issues, especially with the advent of federated machine learning. Training may be performed on edge devices directly, keeping datasets decentralized and private. Additionally, offloading work to different nodes means less computation per device. Projects like GBoard, Siri, and even the medical and military fields already use federated learning. One current open-source framework for federated machine learning is Project Flame (maintained by Cisco Systems). Flame uses object-oriented programming to implement different graphs between edge devices for a federated learning network. Flame can be extended to different kinds of topologies and executed across multiple devices that run Python code using P2P communication.

- Event context: AI Tinkerers Seattle - February 2024 Meetup — 2024-02-29 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_HniMKJNpKf8

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