170 citations · 182 across the 4 of their papers we have counts for
9 papers
Learning Distributionally Robust Models at Scale via Composite Optimization
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi +1
To train machine learning models that are robust to distribution shifts in the data, distributionally robust optimization (DRO) has been proven very effective. However, the existin…
FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching
Farzin Haddadpour, Belhal Karimi, Ping Li +1
Communication complexity and privacy are the two key challenges in Federated Learning where the goal is to perform a distributed learning through a large volume of devices. In this…
Federated Learning with Compression: Unified Analysis and Sharp Guarantees
Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari +1
In federated learning, communication cost is often a critical bottleneck to scale up distributed optimization algorithms to collaboratively learn a model from millions of devices w…
Efficient Fair Principal Component Analysis
Mohammad Mahdi Kamani, Farzin Haddadpour, Rana Forsati +1
It has been shown that dimension reduction methods such as PCA may be inherently prone to unfairness and treat data from different sensitive groups such as race, color, sex, etc.,…
On the Convergence of Local Descent Methods in Federated Learning
Farzin Haddadpour, Mehrdad Mahdavi
In federated distributed learning, the goal is to optimize a global training objective defined over distributed devices, where the data shard at each device is sampled from a possi…
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi +1
Communication overhead is one of the key challenges that hinders the scalability of distributed optimization algorithms. In this paper, we study local distributed SGD, where data i…