6 citations · 14 across the 11 of their papers we have counts for
8 papers · 1 filter
Decentralized Parameter-Free Online Learning with Compressed Gossip
Tomas Ortega, Hamid Jafarkhani
We study decentralized online convex optimization when agents communicate over a graph and messages may be compressed. Classical decentralized online methods typically require lear…
Communication Compression for Distributed Learning with Aggregate and Server-Guided Feedback
Tomas Ortega, Chun-Yin Huang, Xiaoxiao Li +1
Distributed learning, particularly Federated Learning (FL), faces a significant bottleneck in the communication cost, particularly the uplink transmission of client-to-server updat…
Decentralized Parameter-Free Online Learning
Tomas Ortega, Hamid Jafarkhani
We propose the first parameter-free decentralized online learning algorithms with network regret guarantees, which achieve sublinear regret without requiring hyperparameter tuning.…
Communication Compression for Distributed Learning without Control Variates
Tomas Ortega, Chun-Yin Huang, Xiaoxiao Li +1
Distributed learning algorithms, such as the ones employed in Federated Learning (FL), require communication compression to reduce the cost of client uploads. The compression metho…
Quantized and Asynchronous Federated Learning
Tomas Ortega, Hamid Jafarkhani
Recent advances in federated learning have shown that asynchronous variants can be faster and more scalable than their synchronous counterparts. However, their design does not incl…
Decentralized Optimization in Time-Varying Networks with Arbitrary Delays
Tomas Ortega, Hamid Jafarkhani
We consider a decentralized optimization problem for networks affected by communication delays. Examples of such networks include collaborative machine learning, sensor networks, a…
Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation
Tomas Ortega, Hamid Jafarkhani
Asynchronous Federated Learning with Buffered Aggregation (FedBuff) is a state-of-the-art algorithm known for its efficiency and high scalability. However, it has a high communicat…
Gossiped and Quantized Online Multi-Kernel Learning
Tomas Ortega, Hamid Jafarkhani
In instances of online kernel learning where little prior information is available and centralized learning is unfeasible, past research has shown that distributed and online multi…