Want a framework that supports federated learning on the edge, in desktop browsers, integrates well with mobile apps, is performant, and preserves privacy? Welcome to XayNet, written entirely in Rust!
Frameworks for machine learning - including those expressly for federated learning - exist already. These frameworks typically facilitate federated learning of cross-silo use cases - for example in collaborative learning across a limited number of hospitals or for instance across multiple banks working on a common use case without the need to share valuable and sensitive data.
This repository focusses on masked cross-device federated learning to enable the orchestration of machine learning in millions of low-power edge devices, such as smartphones or even cars. By doing this, we hope to also increase the pace and scope of adoption of federated learning in practice and especially allow the protection of end user data. All data remains in private local premises, whereby only encrypted AI models get automatically and asynchronously aggregated. Thus, we provide a solution to the AI privacy dilemma and bridge the often-existing gap between privacy and convenience. Imagine, for example, a voice assistant to learn new words directly on device level and sharing this knowledge with all other instances, without recording and collecting your voice input centrally. Or, think about search engine that learns to personalise search results without collecting your often sensitive search queries centrally… There are thousands of such use cases that right today still trade privacy for convenience. We think this shouldn’t be the case and we want to provide an alternative to overcome this dilemma.
Concretely, we provide developers with:
- App dev tools: An SDK to integrate federated learning into apps written in Dart or other languages of choice for mobile development, as well as frameworks like Flutter.
- Privacy via cross-device federated learning: Train your AI models locally on edge devices such as mobile phones, browsers, or even in cars. Federated learning automatically aggregates the local models into a global model. Thus, all insights inherent in the local models are captured, while the user data stays private on end devices.
- Security Privacy via homomorphic encryption: Aggregate models with the highest security and trust. Xayn’s masking protocol encrypts all models homomorphically. This enables you to aggregate encrypted local models into a global one – without having to decrypt local models at all. This protects private and even the most sensitive data.
Our framework for federated learning is not only a framework for machine learning as such. Rather, it supports the federation of machine learning that takes place on possibly heterogeneous devices and where use cases involve many such devices.
The programming language in which this framework is written should therefore give us strong support for the following:
- Runs "everywhere": the language should not require its own runtime and code should compile on a wide range of devices.
- Memory and concurrency safety: code that compiles should be both memory safe and free of data races.
- Secure communication: state of the art cryptography should be available in vetted implementations.
- Asynchronous communication: abstractions for asynchronous communication should exist that make federated learning scale.
- Fast and functional: the language should offer functional abstractions but also compile code into fast executables.
Rust is one of the very few choices of modern programming languages that meets these requirements:
- its concepts of Ownership and Borrowing make it both memory and thread-safe (hence avoiding many common concurrency issues).
- it has a strong and static type discipline and traits, which describe shareable functionality of a type.
- it is a modern systems programming language, with some functional style features such as pattern matching, closures and iterators.
- its idiomatic code compares favourably to idiomatic C in performance.
- it compiles to WASM and can therefore be applied natively in browser settings.
- it is widely deployable and doesn't necessarily depend on a runtime, unlike languages such as Java and their need for a virtual machine to run its code. Foreign Function Interfaces support calls from other languages/frameworks, including Dart, Python and Flutter.
- it compiles into LLVM, and so it can draw from the abundant tool suites for LLVM.