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Learning from History for Byzantine Robust Optimization
Sai Praneeth Karimireddy, Lie He, Martin Jaggi
Byzantine robustness has received significant attention recently given its importance for distributed and federated learning. In spite of this, we identify severe flaws in existing…
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale +4
Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of the data across different clients which gives rise to the client drift phenomenon. In…
PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning
Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi
Lossy gradient compression has become a practical tool to overcome the communication bottleneck in centrally coordinated distributed training of machine learning models. However, a…
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing
Sai Praneeth Karimireddy, Lie He, Martin Jaggi
In Byzantine robust distributed or federated learning, a central server wants to train a machine learning model over data distributed across multiple workers. However, a fraction o…
Secure Byzantine-Robust Machine Learning
Lie He, Sai Praneeth Karimireddy, Martin Jaggi
Increasingly machine learning systems are being deployed to edge servers and devices (e.g. mobile phones) and trained in a collaborative manner. Such distributed/federated/decentra…