The privacy and security of data are recently research hotspots and challenges.For this issue, an adaptive scheme of distributed learning based on homomorphic encryption and blockchain is proposed. Specifically, in the form of homomorphic encryption, the computing party iteratively aggregates the learning models from distributed participants, so that the privacy of both the data and model is ensured. Moreover, the aggregations are recorded and verified by blockchain, which prevents attacks from malicious nodes and guarantees the reliability of learning. For these sophisticated privacy and security technologies, the computation cost and energy consumption in both the encrypted learning and consensus reaching are analyzed, based on which a joint optimization of computation resources allocation and adaptive aggregation to minimize loss function is established with the realistic solution followed. Finally, the simulations and analysis evaluate the performance of the proposed scheme.

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