Handling data sparsity and model poisoning attacks in federated sequential recommender systems

• Multi-view contrastive learning overcomes data sparsity • Temporal regularisation stabilises user preferences across sequences • Popularity-aware defence mitigates promotion and camouflage attacks • Achieves stable performance within ± 1% even with 80% malicious clients • Maintain 90% of original user accuracy even when cold-start Federated sequential recommendation (FedSeqRec) allows many user devices to train a shared recommender without sending raw interaction histories to a central server, which is important for privacy. However, existing FedSeqRec methods still suffer from two key limitations: (1) most users have very short or sparse histories, especially when they only show interest in a few items for a short period, leaving the model with too little data to understand their preferences; and (2) in a sequential setting it is normal for interests to change suddenly, but the model may misinterpret these abrupt changes as anomalies. In this paper, we propose FORTRESS , a F ederated c O ntrastive R obus T RE commender for S equential S ystems, designed to address these limitations. To tackle the first issue, FORTRESS generates augmented versions of local interaction sequences on each client, so that the model can observe more plausible behaviour patterns and learn user preferences more reliably even when histories are short or sparse. This extra flexibility also gives adversaries more room to manipulate the training signal, so we complement it with a popularity-aware server-side regularizer that discourages rare or suspicious items from drifting into the same embedding clusters as genuinely popular items. To tackle the second issue, we introduce a temporal regularization term that discourages abrupt changes in user representations across adjacent subsequences, allowing the model to adapt to short-term interest shifts while still preserving stable long-term tastes. Experiments on three real-world datasets show that FORTRESS improves recommendation accuracy for sparse and cold-start users and substantially reduces the success of strong model poisoning attacks compared with competitive centralized and federated baselines.