SYNAPSE-Fed: A Bio-Inspired Framework for Continual, Secure, and Explainable AI
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V2I1P110Keywords:
Continual Learning, Meta-Learning, Federated Learning, Explainable AI (XAI), Neuroplasticity, Catastrophic ForgettingAbstract
Artificial intelligence systems deployed in the real world must be capable of continual learning—acquiring new knowledge and skills over time without catastrophically forgetting previously learned information. This challenge is particularly acute in decentralized settings where data is streamed and owned by different entities. Drawing inspiration from the brain’s mech- anisms of neuroplasticity, we introduce SYNAPSE-Fed (Synaptic Network Adaptation for Perpetual Secure Explainable Federated Learning), a novel framework designed for robust continual learning in a federated environment. At its core, SYNAPSE- Fed features a meta-learning algorithm that models synaptic consolidation to mitigate catastrophic forgetting. By identifying and protecting network parameters crucial for past tasks, our algorithm preserves existing knowledge while adapting to new data streams. To ensure the integrity of this learning process across distributed silos, we embed our algorithm within a trust- aware federated learning protocol. A dynamic trust metric evaluates each participant’s contribution based on performance, consistency, and their ability to balance knowledge stability with plasticity, ensuring accountability. Finally, we address the critical need for transparency by introducing a quantitative frame- work to optimize the trade-off between the continual learner’s performance and its explainability. We demonstrate through experiments on continual learning benchmarks that SYNAPSE- Fed significantly outperforms existing methods in preventing catastrophic forgetting and shows high resilience in a federated setting with heterogeneous participants
Downloads
References
[1] M. McCloskey and N. J. Cohen, ”Catastrophic interference in connectionist networks: The sequential learning problem,” in The psychology of learning and motivation, vol. 24, pp. 109-165, Elsevier, 1989.
[2] W. C. Abraham and M. F. Bear, ”Metaplasticity: the plasticity of synaptic plasticity,” Trends in Neurosciences, vol. 19, no. 4, pp. 126-130, 1996.
[3] G. M. van de Ven and A. S. Tolias, ”Three scenarios for continual learning,” arXiv preprint arXiv:1904.07734, 2019.
[4] S.-W. Lee et al., ”Overcoming catastrophic forgetting by incre- mental moment matching,” in Proc. Advances in Neural Informa- tion Processing Systems (NeurIPS), 2017.
[5] J. Kirkpatrick et al., ”Overcoming catastrophic forgetting in neural networks,” in Proc. National Academy of Sciences (PNAS), vol. 114, no. 13, pp. 3521-3526, 2017.
[6] J. Schmidhuber, ”Meta-learning,” in Encyclopedia of Machine Learning, pp. 664-667, Springer, 2011.
[7] Finn, P. Abbeel, and S. Levine, ”Model-agnostic meta-learning for fast adaptation of deep networks,” in Proc. Int. Conf. on Machine Learning (ICML), 2017.
[8] Rusu et al., ”Progressive neural networks,” arXiv preprint arXiv:1606.04671, 2016.
[9] M. D. Riemer et al., ”Learning to learn without forgetting by maximizing transfer and minimizing interference,” in Proc. Int. Conf. on Learning Representations (ICLR), 2019.
[10] H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, ”Communication-efficient learning of deep networks from decentralized data,” in Proc. Int. Conf. on Artificial Intelligence and Statistics (AISTATS), 2017.
[11] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, ”Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
[12] Krizhevsky, ”Learning multiple layers of features from tiny images,” University of Toronto, Tech. Rep., 2009.
[13] Gunning, M. Stefik, J. Choi, T. Miller, S. Stumpf, and G. Yang, “XAI—Explainable artificial intelligence,” Science Robotics, vol. 4, no. 37, 2019.
[14] T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, ”Federated opti- mization in heterogeneous networks,” in Proc. Conf. on Machine Learning and Systems (MLSys), 2020.
[15] V. Mnih et al., ”Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529-533, 2015.
[16] D. Silver et al., ”Mastering the game of Go without human knowledge,” Nature, vol. 550, no. 7676, pp. 354-359, 2017.
[17] Rudin, ”Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” Nature Machine Intelligence, vol. 1, no. 5, pp. 206-215, 2019.
[18] P. Kingma and J. Ba, ”Adam: A method for stochastic optimization,” in Proc. Int. Conf. on Learning Representations (ICLR), 2015.
