Institute for Foundations of Machine Learning

The mathematical formalization of a neurological mechanism in the fruit-fly olfactory circuit as a locality sensitive hash (Flyhash) and bloom filter (FBF) has been recently proposed and "reprogrammed" for various learning tasks such as similarity search, outlier detection and text embeddings. We propose a novel reprogramming of this hash and bloom filter to emulate the canonical nearest neighbor classifier (NNC) in the challenging Federated Learning (FL) setup where training and test data are spread across parties and no data can leave their respective parties. Specifically, we utilize Flyhash and FBF to create the FlyNN classifier, and theoretically establish conditions where FlyNN matches NNC. We show how FlyNN is trained exactly in a FL setup with low communication overhead to produce FlyNNFL, and how it can be differentially private. Empirically, we demonstrate that (i) FlyNN matches NNC accuracy across 70 OpenML datasets, (ii) FlyNNFL training is highly scalable with low communication overhead, providing up to 8x speedup with 16 parties.

High Speed Serial interfaces using differential routing require intra-pair delay matching to minimize loss and electromagnetic interference due to differential to common mode conversion. Existing layout methods using geometric length are useful to avoid gross errors, but fall short as the electrical length is a non-linear 3D problem that is dependent on self-coupling and return path. This paper investigates this issue and compares different regression models to improve skew compensation accuracy and speed.