  Neural network pruning is an essential technique for reducing the size and
complexity of deep neural networks, enabling large-scale models on devices with
limited resources. However, existing pruning approaches heavily rely on
training data for guiding the pruning strategies, making them ineffective for
federated learning over distributed and confidential datasets. Additionally,
the memory- and computation-intensive pruning process becomes infeasible for
recourse-constrained devices in federated learning. To address these
challenges, we propose FedTiny, a distributed pruning framework for federated
learning that generates specialized tiny models for memory- and
computing-constrained devices. We introduce two key modules in FedTiny to
adaptively search coarse- and finer-pruned specialized models to fit deployment
scenarios with sparse and cheap local computation. First, an adaptive batch
normalization selection module is designed to mitigate biases in pruning caused
by the heterogeneity of local data. Second, a lightweight progressive pruning
module aims to finer prune the models under strict memory and computational
budgets, allowing the pruning policy for each layer to be gradually determined
rather than evaluating the overall model structure. The experimental results
demonstrate the effectiveness of FedTiny, which outperforms state-of-the-art
approaches, particularly when compressing deep models to extremely sparse tiny
models. FedTiny achieves an accuracy improvement of 2.61% while significantly
reducing the computational cost by 95.91% and the memory footprint by 94.01%
compared to state-of-the-art methods.
