FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data
This addresses communication bottlenecks in federated learning for non-IID data, offering a scalable optimization solution, though it is incremental as it builds on existing methods like AMSGrad.
The paper tackles the inefficiency of federated learning with heterogeneous data by proposing FedLALR, a method where each client adapts its learning rate locally, achieving linear speedup in convergence and showing efficacy in CV and NLP tasks.
Federated learning is an emerging distributed machine learning method, enables a large number of clients to train a model without exchanging their local data. The time cost of communication is an essential bottleneck in federated learning, especially for training large-scale deep neural networks. Some communication-efficient federated learning methods, such as FedAvg and FedAdam, share the same learning rate across different clients. But they are not efficient when data is heterogeneous. To maximize the performance of optimization methods, the main challenge is how to adjust the learning rate without hurting the convergence. In this paper, we propose a heterogeneous local variant of AMSGrad, named FedLALR, in which each client adjusts its learning rate based on local historical gradient squares and synchronized learning rates. Theoretical analysis shows that our client-specified auto-tuned learning rate scheduling can converge and achieve linear speedup with respect to the number of clients, which enables promising scalability in federated optimization. We also empirically compare our method with several communication-efficient federated optimization methods. Extensive experimental results on Computer Vision (CV) tasks and Natural Language Processing (NLP) task show the efficacy of our proposed FedLALR method and also coincides with our theoretical findings.