Federated Class-Incremental Learning (FCIL) enables distributed models to continuously learn new categories while preserving privacy, which suffers from the problem of catastrophic forgetting. To address this issue, generative replay has emerged as a mainstream solution, yet its performance is hampered by two fundamental bottlenecks: (1) low-fidelity synthesis, where generated visual samples fail to effectively represent historical knowledge, and (2) class imbalance in FCIL, which undermines fair learning across classes. In this paper, we propose a novel generative replay framework called FedART (Federated Adaptive Replay with Text-anchors). To combat low-fidelity synthesis, FedART employs a text-anchored initialization strategy. Instead of optimizing from a random start, this approach provides strong semantic priors to guide the generation process. To tackle class imbalance, we design a dual adaptive aggregation mechanism. This mechanism applies tailored weighting strategies at both the generator and classifier levels, leveraging local training dynamics to ensure both the quality of generative knowledge and the fairness of classifier aggregation. Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate that FedART significantly outperforms state-of-the-art methods, achieving an accuracy of up to 43.62% and establishing a new and robust benchmark for enhancing the effectiveness of generative replay in FCIL.

Federated learning has emerged as a promising paradigm for large-scale collaborative training tasks, harnessing diverse local datasets from different clients to jointly train global models. In real-world implementations, client data could have label noise, causing the quality of the global model to be influenced. Existing label-correction solutions assume all the clients are discreet and fail to consider detecting the malicious clients, thus are not practical or privacy-preserving. In this paper, we present zkCor, an efficient and reliable label noise correction scheme with zero-knowledge confidentiality. Our method is designed upon FedCorr [1], but with more relaxed security assumptions. zkCor is established from the ingenious synergy of the label noise correction protocol and the zero-knowledge proof (ZKP), requiring each client to provide a computation integrity proof to the aggregator in each iteration. Thus, clients are forced to jointly guarantee label-correction reliability. We further devise a batch ZKP that is efficient and more suitable for federated learning settings. We rigorously illustrate the building blocks of zkCor and complete the prototype implementation. The extensive experiments demonstrate that zkCor can gain at least 2 to 30 times better performance than the baseline approach on verification workloads with nearly no extra proof time cost from clients.

Decentralized storage platforms based on consortium blockchains have emerged in the spotlight of research and industry communities because they are flexible, transparent, and eliminated trust in contrast to the traditional centralized data-sharing model. However, due to wide attacking surfaces in a blockchain network, this decentralized data-sharing paradigm is subject to malicious data breaches. Untrusted blockchain nodes can directly obtain sensitive information from the query processing and their local storage. Several studies have been made for solving this dilemma, but they only focus on single-user settings and cannot be directly applied to multi-owners blockchain-based data sharing scenarios. In this paper, we introduce FedEDB, a federated and encrypted data store by using consortium blockchains. Unlike existing solutions that focus on single-user settings, our proposed schemes can efficiently support privacy-preserving and reliable multi-owner queries in the decentralized setting. We start from the practical key aggregation technique to construct the multi-owner search schemes and further refine the underling building blocks to enhance the security. Besides, we integrate the smart contract with our tailored zero-knowledge proof to enforce secure and reliable result verification protocol with fairness. We implement a prototype and thorough security analysis and comprehensive evaluation results confirm the practicability of our design.