TAMUNA: New Algorithm Combines Local Training and Compression for Efficient Distributed Optimization with Partial Client Participation
Researchers have introduced TAMUNA, a distributed optimization algorithm that combines local training, gradient compression, and partial client participation for federated learning. Prior methods achieving doubly accelerated communication rates required all clients to participate in every round, a constraint that made them impractical in real-world deployments where devices frequently go offline. TAMUNA addresses this critical gap, potentially enabling more efficient and robust federated learning systems across heterogeneous device networks.
TAMUNA (presented in arXiv preprint 2302.09832, now in its fourth revision) is a new algorithm designed for distributed optimization and federated learning that tackles the communication bottleneck between parallel devices and a central server. Two established strategies for reducing this bottleneck—local training, which cuts communication frequency, and compression, which reduces the size of transmitted data—have previously been combined to achieve doubly accelerated convergence rates with respect to both condition number and model dimension. However, all prior methods combining these two strategies required full client participation, meaning they failed whenever any device missed a communication round. TAMUNA overcomes this limitation by decoupling primal model updates from dual control variates, resolving what the authors describe as an architectural deadlock in earlier approaches. In the strongly convex setting, the algorithm is proven to converge linearly to the exact solution while supporting arbitrary levels of partial participation. The work establishes a new state of the art in communication-efficient federated learning and has undergone multiple revisions since its initial submission in February 2023.
What's missing
The paper's theoretical guarantees are established under the strongly convex setting; convergence behavior for non-convex objectives (common in deep learning) is not addressed. Empirical benchmarks on large-scale real-world federated learning tasks are not described in the abstract, leaving practical performance gains versus prior methods unquantified.
What different sources said
- arXiv cs.LGCenter
TAMUNA: Doubly Accelerated Distributed Optimization under Partial Participation
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