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Distributed Broad Learning System With Quantized and Censored Communications

As a novel neural network model, the broad learning system (BLS) has been used extensively due to its simple structure and few model hyperparameters. This paper presents a distributed BLS algorithm with quantized and censored communications (DQC-BLS). The DQC-BLS algorithm is based on the alternating direction method of multipliers (ADMM). In order to reduce the high communication cost, the DQC-BLS algorithm improves the communication mechanism between the in-networks agents by introducing quantization and communication-censoring strategies. Quantization reduces the number of bits for each transmission whereas communication censoring reduces the number of communications. The experimental results verify that the DQC-BLS algorithm can reduce communication costs while maintaining similar performance on the test set.