AFCL solves federated continual learning without gradients by aggregating local Gram matrices and label statistics, proving exact spatio-temporal invariance: the global model equals centralized joint learning for any data partition.
FedGMKD: An efficient prototype feder- ated learning framework through knowledge distillation and discrepancy-aware aggregation
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AFCL: Analytic Federated Continual Learning for Spatio-Temporal Invariance of Non-IID Data
AFCL solves federated continual learning without gradients by aggregating local Gram matrices and label statistics, proving exact spatio-temporal invariance: the global model equals centralized joint learning for any data partition.