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This letter proposes a novel communication-efficient and privacy-preservingdistributed machine learning framework, coined Mix2FLD. To addressuplink-downlink capacity asymmetry, local model outputs are uploaded to aserver in the uplink as in federated distillation (FD), whereas global modelparameters are downloaded in the downlink as in federated learning (FL). Thisrequires a model output-to-parameter conversion at the server, after collectingadditional data samples from devices. To preserve privacy while notcompromising accuracy, linearly mixed-up local samples are uploaded, andinversely mixed up across different devices at the server. Numericalevaluations show that Mix2FLD achieves up to 16.7% higher test accuracy whilereducing convergence time by up to 18.8% under asymmetric uplink-downlinkchannels compared to FL.

Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup