Federated Principal Component Analysis
This addresses the need for scalable and private data analysis in distributed environments, though it is incremental as it builds on existing federated and PCA methods.
The paper tackles the problem of performing principal component analysis (PCA) in a federated, memory-limited setting with privacy guarantees, resulting in an algorithm that improves upon state-of-the-art differential privacy bounds and matches or outperforms traditional non-federated methods in simulations.
We present a federated, asynchronous, and $(\varepsilon, δ)$-differentially private algorithm for PCA in the memory-limited setting. Our algorithm incrementally computes local model updates using a streaming procedure and adaptively estimates its $r$ leading principal components when only $\mathcal{O}(dr)$ memory is available with $d$ being the dimensionality of the data. We guarantee differential privacy via an input-perturbation scheme in which the covariance matrix of a dataset $\mathbf{X} \in \mathbb{R}^{d \times n}$ is perturbed with a non-symmetric random Gaussian matrix with variance in $\mathcal{O}\left(\left(\frac{d}{n}\right)^2 \log d \right)$, thus improving upon the state-of-the-art. Furthermore, contrary to previous federated or distributed algorithms for PCA, our algorithm is also invariant to permutations in the incoming data, which provides robustness against straggler or failed nodes. Numerical simulations show that, while using limited-memory, our algorithm exhibits performance that closely matches or outperforms traditional non-federated algorithms, and in the absence of communication latency, it exhibits attractive horizontal scalability.