arXiv
Open Access
2026
Computationally Efficient Laplacian CL-colME
Nikola Stankovic
Abstrak
Decentralized collaborative mean estimation (colME) is a fundamental task in heterogeneous networks. Its graph-based variants B-colME and C-colME achieve high scalability of the problem. This paper evaluates the consensus-based C-colME framework, which relies on doubly stochastic averaging matrices to ensure convergence to the oracle solution. We propose CL-colME, a novel variant utilizing Laplacian-based consensus to avoid the computationally expensive normalization processes. Simulation results show that the proposed CL-colME maintains the convergence behavior and accuracy of C-colME while improving computational efficiency.
Penulis (1)
N
Nikola Stankovic
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2026
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓