4 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
Differentially-Private Multi-Tier Federated Learning
Evan Chen, Frank Po-Chen Lin, Dong-Jun Han +1
While federated learning (FL) eliminates the transmission of raw data over a network, it is still vulnerable to privacy breaches from the communicated model parameters. In this wor…
Delay-Aware Hierarchical Federated Learning
Frank Po-Chen Lin, Seyyedali Hosseinalipour, Nicolò Michelusi +1
Federated learning has gained popularity as a means of training models distributed across the wireless edge. The paper introduces delay-aware hierarchical federated learning (DFL)…
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling
Frank Po-Chen Lin, Seyyedali Hosseinalipour, Sheikh Shams Azam +2
Federated learning has emerged as a popular technique for distributing model training across the network edge. Its learning architecture is conventionally a star topology between t…
Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations
Frank Po-Chen Lin, Seyyedali Hosseinalipour, Sheikh Shams Azam +2
Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge. In this paper, we propose two timescale hybrid…
Federated Learning with Communication Delay in Edge Networks
Frank Po-Chen Lin, Christopher G. Brinton, Nicolò Michelusi
Federated learning has received significant attention as a potential solution for distributing machine learning (ML) model training through edge networks. This work addresses an im…