64 citations · 252 across the 64 of their papers we have counts for
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MPQ-Diff: Mixed Precision Quantization for Diffusion Models
Rocco Manz Maruzzelli, Basile Lewandowski, Lydia Y. Chen
Diffusion models (DMs) generate remarkable high quality images via the stochastic denoising process, which unfortunately incurs high sampling time. Post-quantizing the trained diff…
Federated Time Series Generation on Feature and Temporally Misaligned Data
Zhi Wen Soi, Chenrui Fan, Aditya Shankar +2
Distributed time series data presents a challenge for federated learning, as clients often possess different feature sets and have misaligned time steps. Existing federated time se…
Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients
Yuncong Zuo, Bart Cox, Lydia Y. Chen +1
Federated learning (FL) systems enable multiple clients to train a machine learning model iteratively through synchronously exchanging the intermediate model weights with a single…
Asynchronous Byzantine Federated Learning
Bart Cox, Abele Mălan, Lydia Y. Chen +1
Federated learning (FL) enables a set of geographically distributed clients to collectively train a model through a server. Classically, the training process is synchronous, but ca…
Parameterizing Federated Continual Learning for Reproducible Research
Bart Cox, Jeroen Galjaard, Aditya Shankar +2
Federated Learning (FL) systems evolve in heterogeneous and ever-evolving environments that challenge their performance. Under real deployments, the learning tasks of clients can a…
Gradient Inversion of Federated Diffusion Models
Jiyue Huang, Chi Hong, Lydia Y. Chen +1
Diffusion models are becoming defector generative models, which generate exceptionally high-resolution image data. Training effective diffusion models require massive real data, wh…