Archgen AI أُعيد تغريده
Our paper on Privacy Preserving Load Forecasting via Federated Learning just got published!!🥳🥳
Personalized AI for energy is finally leaving the centralized silo.
Your home’s power data doesn't have to live in a utility’s database to be useful.
We show how to train high-accuracy load models across thousands of smart meters without ever moving the raw usage data. Instead, it ships "noisy" model updates and keeps the personal habits local.
It's not just keeping data private, it’s making the model actually better by letting it learn your specific routine.
When a utility predicts power demand, it usually needs to see exactly when you turn on your dryer or AC.
That is a massive privacy risk. High-res meter data is basically a map of your life.
The conventional fix is Federated Learning (FL), where the model goes to the data. But standard FL has a "heterogeneity" problem, it struggles because every house uses power differently.
Our paper introduces DP-SA-FedPer: an architecture that solves the privacy-utility-efficiency trilemma.
Differential Privacy (DP): It adds mathematical "noise" to model updates so no one can reverse-engineer your habits from the weights.
Secure Aggregation (SA): The server only sees the total sum of updates, never an individual household’s contribution.
Lightweight Personalization: The model is split. A global "base" learns general grid trends, while a local "head" stays on your meter to learn your specific 2 AM laundry habit.
> 95.2% Accuracy (outperforming standard centralized models).
> 12% lower communication cost than baseline FL.
> 6.5% boost specifically from the personalization layer.
Privacy isn't a tax on performance anymore. By combining secure aggregation with local "heads," we can build a grid that is more efficient, more scalable, and respects the front door.
@naveen_venk @archgen_
English
