• Computer Science > Distributed, Parallel, and Cluster Computing [Submitted on 25 Feb 2026] Title:Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks View PDF HTML (experimental)Abstract:In this paper, we investigate a problem of minimizing total energy consumption for secure federated learning (FL) over wireless edge networks • To address the high computational cost and privacy challenges in conventional FL with neural networks (NN) for resource-constrained users, we propose a novel FL with hyperdimensional computing and differential privacy (FL-HDC-DP) framework • In the considered model, each edge user employs hyperdimensional computing (HDC) for local training, which replaces complex neural updates with simple hypervector operations, and applies differential privacy (DP) noise to protect transmitted model information • We optimize the total energy of computation and communication under both latency and privacy constraints • We formulate the problem as an optimization that minimizes the total energy of all users by jointly allocating HDC dimension, transmission time, system bandwidth, transmit power, and CPU frequency

Article Summaries:

  • Computer Science > Distributed, Parallel, and Cluster Computing [Submitted on 25 Feb 2026] Title:Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks View PDF HTML (experimental)Abstract:In this paper, we investigate a problem of minimizing total energy consumption for secure federated learning (FL) over wireless edge networks. To address the high computational cost and privacy challenges in conventional FL with neural networks (NN) for resource-constrained users, we propose a novel FL with hyperdimensional computing and differential privacy

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