Cost-Efficient Federated Learning for Edge Intelligence in Multi-Cell Networks

The proliferation of various mobile devices with massive data and improving computing capacity have prompted the rise of edge artificial intelligence (Edge AI). Without revealing the raw data, federated learning (FL) becomes a promising distributed learning paradigm that caters to the above trend. Nevertheless, due to periodical communication for model aggregation, it would incur inevitable costs in terms of training latency and energy consumption, especially in multi-cell edge networks. Thus motivated, we study the joint edge aggregation and association problem to achieve the cost-efficient FL performance, where the model aggregation over multiple cells just happens at the network edge. After analyzing the NP-hardness with complex coupled variables, we transform it into a set function optimization problem and prove the objective function shows neither submodular nor supermodular property. By decomposing the complex objective function, we reconstruct a substitute function with the supermodularity and the bounded gap. On this basis, we design a two-stage search-based algorithm with theoretical performance guarantee. We further extend to the case of flexible bandwidth allocation and design the decoupled resource allocation algorithm with reduced computation size. Finally, extensive simulations and field experiments based on the testbed are conducted to validate both the effectiveness and near-optimality of our proposed solution.