Stich, Sebastian U.

Advances and Open Problems in Federated Learning

Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al. (2019) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 7,857

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., & Suresh, A. T. (2019) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 1,734

Sparsified SGD with Memory

Stich, S. U., Cordonnier, J.-B., & Jaggi, M. (2018) · arXiv

Stochastic Gradient Optimization TechniquesDistributed Control Multi-Agent Systems

Cited by 508

Is Local SGD Better than Minibatch SGD?

Woodworth, B., Patel, K. K., Stich, S. U., Dai, Z., Bullins, B., McMahan, H. B., Shamir, O., & Srebro, N. (2020) · arXiv

Stochastic Gradient Optimization TechniquesDistributed Control Multi-Agent Systems

Cited by 131

Dynamic Model Pruning with Feedback

Lin, T., Stich, S. U., Barba, L., Dmitriev, D., & Jaggi, M. (2020) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 126

Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Karimireddy, S. P., Jaggi, M., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., & Suresh, A. T. (2020) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 96