Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
3
0
2
Analog over-the-air model aggregation can match the theoretical $\mathcal{O}(1/\sqrt{T})$ convergence of ideal FedAvg without requiring instantaneous channel state information or strict phase alignment.
Uniform weighting in decentralized optimization can yield a vanishing tracking error, but discounted strategies may trap you in a persistent bias floor.
The resource requirements for decentralized gradient descent reveal surprising trade-offs that could redefine how we approach distributed optimization in complex networks.