danieljanes

23 ms·

1.
by danieljanes 6mo ago
We're not "still" using Markdown, we're only getting started. Markdown is only going to get more popular as AI agents usage grows.
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Flower Labs and Starcloud Reach a Major AI Milestone in Orbit(flower.ai)
6 points by danieljanes 7mo ago|1 comments
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by danieljanes 7mo ago
Flower Labs and Starcloud are sharing a major milestone: the successful execution of a decentralized AI workload using Flower on an operational Starcloud satellite.
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by danieljanes 8mo ago
ViT (Vision Transformer) fine-tuned on a Starcloud satellite in space (using the Flower framework) -- to the best of our knowledge, this is a world first: https://flower.ai/blog/2026-02-02-flower-labs-and-starcloud-...
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by danieljanes 2y ago
Not having to install CUDA is a killer feature, looking forward to DGX OS
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by danieljanes 2y ago
Given the code quality and rigid testing, SQLite is probably the last project that should be rewritten. It'd be great to see all other C code rewritten first!
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Flower 1.12 release (open-source decentralized AI)(flower.ai)
7 points by danieljanes 2y ago|0 comments
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Flower 1.11: Auto-deploy code, isolate ClientApp execution(flower.ai)
9 points by danieljanes 2y ago|0 comments
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New short course (2/2): Federated Fine-tuning of LLMs on Private Data(learn.deeplearning.ai)
11 points by danieljanes 2y ago|0 comments
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New short course (1/2): Intro to Federated (Machine) Learning(deeplearning.ai)
10 points by danieljanes 2y ago|0 comments
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Flower 1.10(discuss.flower.ai)
7 points by danieljanes 2y ago|0 comments
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Flower 1.9 (a friendly federated learning framework)(discuss.flower.ai)
8 points by danieljanes 2y ago|0 comments
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Flower 1.8(flower.ai)
8 points by danieljanes 2y ago|0 comments
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by danieljanes 2y ago
We use Pyenv successfully for developing the Flower open-source project. We use a few simple Bash scripts to manage virtual environments with different Python versions via pyenv and the pyenv-virtualenv plugin. The main scripts are `venv-cr
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Flower 1.7 (train AI on distributed data)(flower.dev)
5 points by danieljanes 3y ago|0 comments
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by danieljanes 3y ago
The big opportunity on the edge is access to more data. Especially with the rise of end-to-end encryption, applications will be able to use more (and more diverse) data on the edge to get better model performance. It's generally true t
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by danieljanes 3y ago
I can confirm that we're seeing 2x to 3x faster (RPi 4 vs RPi 5) in some of our early tests
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by danieljanes 3y ago
One of the Flower maintainers here. The code example is primarily meant as a demonstrator to show that it's possible to fine-tune these models in a federated way on devices as small as a Raspberry Pi 5. The bigger takeaway is that we&#
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by danieljanes 3y ago
One of the Flower maintainers here, we're planning to follow up with a more in-depth performance comparison soon
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Federated finetuning of Whisper on Raspberry Pi 5(flower.dev)
90 points by danieljanes 3y ago|20 comments
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by danieljanes 3y ago
Does GGML support training on the edge? We're especially interested in training support for Android+iOS
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by danieljanes 3y ago
One of the creators of Flower here - I can only say that the team behind Flower appreciates the contributions of FedML to the field of federated learning. Their work helps to make federated learning more widely known, and they published sig
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by danieljanes 3y ago
Indeed - looking forward to this
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by danieljanes 3y ago
Thanks for adding this here! We added these DP wrappers, and we're working on something similar for Secure Aggregation, but I must admit that we have to document them better to make using them easier for everyone
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by danieljanes 3y ago
Great to hear, thanks for sharing - modularity, scalability, and user friendliness are what we think a lot about :)
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by danieljanes 3y ago
Thanks! We're huge fans of the work that PySyft is doing, and we're very supportive of the Flower PySyft integration.
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by danieljanes 3y ago
There are some similarities, but also some differences. Flower's take is that it wants to support the entire FL workflow from experimental research to large-scale production deployments and operation. Some other FL frameworks fall eith
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by danieljanes 3y ago
Hi there - the data never moves if you train a model using federated learning. It stays on user devices or in organizational silos. After the training, you have the model parameters of the model on the server, without the server having ever
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by danieljanes 3y ago
Thanks, we're excited too! Flower tries to automate this as much as it can. In cases where multiple organizations are involved, the workload can run in a fully automated manner if that's fine for all organizations. If a review ste
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by danieljanes 3y ago
Agreed that this is an interesting direction. The core Flower abstractions are "federated learning agnostic", which means that they can be used for different kinds of distributed/federated workloads, not just federated learni

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