The Journal IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) has achieved a 2024 Impact Factor of 18.6, and is the top tier journal in the CAS system. Professor Zhou Xingcai and his team published the paper, entitled “FedFask: Fast Sketching Distributed PCA for Large-Scale Federated Data”, in this prestigious journal. The abstract and link to the full paper are provided below.
FedFask: Fast Sketching Distributed PCA for Large-Scale Federated Data
Professor Xingcai Zhou; Guang Yang; Haotian Zheng; Linglong Kong; Jinde Cao
Abstract:We study distributed principal component analysis (PCA) for large-scale federated data when the sample size n and dimension d are both ultra-large. This type of data is currently very common, but faces numerous challenges in PCA learning, such as communication overhead and computational complexity. We develop a new algorithm FedFask (Fast Sketching for Federated learning) with lower communication cost 
and lower computational complexity
, where m is the number of workers, r is the rank of matrix, p is the dimension of sketched column space, and 
. In FedFask, we adopt and develop technologies such as fast sketching, alignments with orthogonal Procrustes Fixing, and matrix Stiefel manifold via Kolmogorov-Nagumo-type average. Thus, FedFask has a higher accuracy, lower stochastic variation, and best representation of multiple randomly projected eigenspaces, and avoids the orthogonal ambiguity of eigenspaces. We show that FedFask achieves the same rate of learning 
as the centralized PCA uses all data, and tolerates more workers to parallel acceleration computation. We conduct extensive experiments to demonstrate the effectiveness of FedFask.
