Federated Learning (FL) is a collaborative machine learning paradigm allowing participants to train a global model collaboratively without sharing training data. The distributed nature makes FL vulnerable to the untargeted or backdoor model poisoning attacks (MPAs). Hence, lots of defense methods are proposed to secure FL. However, existing defenses are ineffective in defending against the emerging stealthy layer-space MPA, since the defenses either focus on the model space or ignore the disparities between the layers. In this paper, we propose a novel layer-space defense method called FLGuardian that can protect the global model from the state-of-the-art MPAs. FLGuardian first employs a new layer-wise detection to find out the benign clients for each layer through pairwise cosine distances and pairwise Euclidean distances combined with a clustering algorithm. Then, FLGuardian assigns a trust score for each client according to the detection results of all the layers, where a deeper layer in the model brings a higher weight in the scoring. Finally, we select several clients with the highest scores for updating the global model. Experimental results show that FLGuardian excels nine typical defense methods against seven state-of-the-art MPAs in most cases. Particularly, under LPattack, the emerging layer-space backdoor MPA, FLGuardian secures Backdoor Success Rate (BSR) below 3% while other defenses have over 93% BSRs on CIFAR-10. Moreover, FLGuardian remains robust against adaptive attacks tailored to FLGuardian.

Data deduplication is promised to extend the lifetime and capacity of storage on mobile devices. However, existing data deduplication works show high memory consumption and indexing costs for maintaining a fingerprint for each data block, especially when the duplicate ratio of data blocks on mobile systems is about 10% to 30%. In this paper, we propose a novel approach called FinerDedup to optimize the memory costs and retrieval efficiency of data deduplication. FinerDedup drastically reduces the number of fingerprints by screening out the duplicate data blocks via random forest and Bloom filter. We implement FinerDedup on real mobile devices with Android 10 and evaluate it with real workloads. Extensive experimental results show that FinerDedup can reduce 85% of fingerprints and 20% of I/O latency over the widely-used DmDedup.

Language scaling aims to deploy Natural Language Processing (NLP) applications economically across many countries/regions with different languages. Language scaling has been heavily invested by industry since many parties want to deploy their applications/services to global markets. At the same time, scaling out NLP applications to various languages, essentially a data science problem, remains a grand challenge due to the huge differences in the morphology, syntaxes, and pragmatics among different languages. We present a comprehensive survey and tutorial on language scaling. We start with a clear problem description for language scaling and an intuitive discussion on the overall challenges. Then, we outline two major categories of approaches to language scaling, namely, model transfer and data transfer. We present a taxonomy to summarize various methods in literature. A large part of the tutorial is organized to address various types of NLP applications. Finally, we discuss several important challenges in this area and future directions.