Federated learning trains a shared model across many clients without moving raw data, while continual learning learns a stream of tasks and mitigates catastrophic forgetting. Continual federated learning combines these goals but is challenged by non-IID label skew and forgetting under evolving client data. We propose GAN-CFL (Generative Adversarial Networks-based Continual Federated Learning) to tackle these challenges. GAN-CFL enables clients to learn from new data, without storing historical data, and effectively adapts to non-IID data distributions. GAN-CFL enhances data heterogeneity by generating synthetic data to augment real datasets and mitigates catastrophic forgetting across multiple clients by incorporating elastic weight consolidation algorithm. In this framework, the generator produces synthetic images, while the discriminator classifies both real and generated images. The trained discriminator is then used as a classifier on real data to provide accuracy metrics. The global model aggregates local weights from clients to optimize overall performance. We evaluate GAN-CFL on six datasets, MNIST, K-MNIST, Fashion-MNIST, EMNIST-letters, EMNIST-Balanced, and CIFAR-10 for experiments involving up to 100 clients. Our model is compared to a centralized learning method for ablation analysis. The results show that GAN-CFL outperforms existing methods in classification accuracy.

Abhinav Joshi, Shounak Paul, Akshat Sharma, Pawan Goyal, Saptarshi Ghosh, Ashutosh Modi. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

The task of Prior Case Retrieval (PCR) in the legal domain is about automatically citing relevant (based on facts and precedence) prior legal cases in a given query case.To further promote research in PCR, in this paper, we propose a new large benchmark (in English) for the PCR task: IL-PCR (Indian Legal Prior Case Retrieval) corpus.Given the complex nature of case relevance and the long size of legal documents, BM25 remains a strong baseline for ranking the cited prior documents.In this work, we explore the role of events in legal case retrieval and propose an unsupervised retrieval method-based pipeline U-CREAT (Unsupervised Case Retrieval using Events Extraction).We find that the proposed unsupervised retrieval method significantly increases performance compared to BM25 and makes retrieval faster by a considerable margin, making it applicable to real-time case retrieval systems.Our proposed system is generic, we show that it generalizes across two different legal systems (Indian and Canadian), and it shows state-ofthe-art performance on the benchmarks for both the legal systems (IL-PCR and COLIEE corpora).