• A simple and effective multi-site brain disease recognition framework based on tensor decomposition and personalized federated learning is proposed to quickly integrate samples from different hospitals/sites while enabling personalized feature extraction at each site. • A designed Dynamic Prototype Aggregation (DPA) module utilizes a sliding window technique to capture the intrinsic characteristics of time-varying BOLD signals. • A dual-feature aggregation module is designed to aggregate coarse-grained shared features and fine-grained prototype representation features, respectively, to facilitate efficient knowledge sharing among sites. Brain diseases significantly impact physical and mental health, making the development of models to identify biomarkers for early diagnosis essential. However, building high-quality models typically relies on large-scale datasets, while the privacy-sensitive nature of medical data often restricts its sharing and utilization. Multi-site studies provide a potential solution by integrating data from various sources, yet existing methods frequently neglect site-specific private features, such as demographic information. Therefore, in this paper, we propose a simple yet effective framework based on Tensor Decomposition and Personalized Federated Learning (TDPFL) for multi-site brain disease recognition, while protecting these private features. On the central server, we designed a dual feature aggregation module to facilitate efficient knowledge sharing among sites. On the client side, we introduced a personalized branch to safeguard private information ( i.e. , age, gender, and education) and developed a tensor decomposition module to extract features from subjects’ brain scan data. Furthermore, we developed a dynamic prototype aggregation module to monitor evolving brain features over time. This mechanism enhances the model’s capacity to capture these dynamics, thereby improving classification and prediction accuracy. Experiments on two publicly available rs-fMRI datasets across six sites showed that TDPFL outperformed baseline methods with a 4 % improvement in average classification accuracy. Additionally, we identified site-specific brain disease-related biomarkers, offering novel insights into early diagnosis. Code is available at https://github.com/ChaojunZ/TDPFL.git

Transformer-based text-image matching model, known as CLIP, has garnered significant attention owing to its exceptional performance in text-image retrieval tasks and downstream applications. However, the interpret-ability of CLIP remains underexplored. Existing interpretation methods for Transformers often struggle with incomplete and unreliable attributions within the image and text modalities, respectively. In this paper, we propose a fine-grained interpretation method, termed Class Activation Values (CAV), to provide lucid and faithful visual explanations for CLIP-based text-image retrievals. Specifically, we systematically perform multi-scale accumulation and fusion of class-specific gradients and activation value features to generate high-definition explanations for the image encoder. Furthermore, we present element-wise gradient-based weights to attribute fine-grained relevance between value features and output similarity within the text encoder. The proposed CAV is capable of simultaneously rendering detailed and credible explanations due to its precise feature attribution. Extensive qualitative and quantitative experiments are conducted on the ImageNet-1k and MS COCO datasets, and the experimental results demonstrate that CAV outperforms state-of-the-art interpretation methods in both faithfulness and localization assessments across image and text modalities.

The continual emergence of new entities and relations drives the dynamic expansion of knowledge graphs (KG). In the face of such growing KG, relearning from scratch wastes acquired knowledge, while learning solely from new snapshots leads to model forgetting of old knowledge. Existing methods focus on lifelong learning in growing KG through transfer and regularize embeddings. However, extensive entity updates to adapt to new snapshots introduce conflicts between old and new knowledge, thereby resulting in the inevitable occurrence of knowledge forgetting. To address these challenges, we propose the Evolutionary Relation Path Passing (ERPP) model for lifelong knowledge graph embedding, aiming to shift from knowledge forgetting to knowledge accumulation, thereby achieving accurate long-term prediction. Specifically, we propose a snapshot conditional relation path passing strategy to generate expressive representations that better adapt to snapshots compared to the transferred embeddings in existing methods. Subsequently, we propose a relation inheritance and evolution mechanism across snapshots and continue relation path passing in next snapshots. This allows ERPP to avoid inevitable catastrophic forgetting from frequent entity embedding updates. ERPP outperforms SOTA models in 35 scenarios, with average improvements of 11.1% in long-term prediction and 12.9% in knowledge transfer. Moreover, ERPP makes a breakthrough in achieving knowledge positive accumulation, in contrast to the negative forgetting of existing models. To the best of our knowledge, ERPP is the first model to realize knowledge accumulation. Our code is available at https://anonymous.4open.science/r/ERPP-6D66.

Modern data center network often possesses multiple end-to-end parallel paths, which undertake the crucial task of transmitting vast heterogeneous data traffic generated by a wide variety of applications. To fully utilize the offered super high bisection network bandwidth thus benefiting application performance, many data center load balancing schemes are proposed to improve path utilization for avoiding network congestion hot-spot. However, these schemes are naturally agnostic to data center traffic pattern and the diverse requirements on flow-transmission, leading to the sub-optimal network transmission performance. To address this issue, this paper presents a new data center load balancing scheme, called PCLB, which selectively generates Clone Packets by considering both flow-transmission phases and path states, thereby helping different types of flows choose more appropriate paths for speeding up their data transmission. Experimental results of numerous NS2 simulations show that PCLB significantly reduces the average and tail flow completion time for delay-sensitive flows, while the performance of throughput-oriented flows can be always maintained at high level.

Modern data centers are hosting a great number of various applications (e.g. MapReduce and web search) that require a high fan-in data communication, which easily causes serious packet losses and timeouts, substantially degrading the application performance. To address this issue, various host-based and switch-based transport protocols are proposed to eliminate timeout and improve the user experience. Unfortunately, although existing transport protocols can effectively eliminate the timeout, they inevitably result in persistent queueing backlog and degrade the network performance, especially delay-sensitive short flows. To this end, we propose a general scheme with ultra-low latency called UL2to address the above problem. Concretely, the sender periodically estimates the queueing delay of each packet on the transmission path and senses the degree of congestion based on its measured result. Then the sender timely yet cautiously executes a pausing transmission operation based on measured queueing delay, guaranteeing fast elimination of queue delays and high link utilization. Our evaluation indicates that UL2can effectively eliminate queue backlog and reduce the queueing delay by more than 90%. Moreover, UL2enhances the performance of state-of-the-art transport protocols in terms of flow completion times by up to 44.98%.

Increasing diversity of applications and services are being migrated to modern data center networks (DCNs), and these applications and services are typically generating various combinations of long and short flows with or without deadlines. However, most existing flow scheduling solutions for DCNs either adopt single-queue strategy (e.g., D2TCP) that inevitably results in non-urgent flows blocking urgent flows or multi-queue mechanism (e.g., Aemon) that is at the cost of packet reordering. In this paper, we present DPOT, a Dynamic Priority-based Ordered Transmission mechanism that is aimed at minimizing the flow completion time and deadline missing rate. To avoid the urgent flows being blocked by the non-urgent flows, the DPOT switch adopts different priority queues to buffer packets for different types of flows. What’s more, when the switch detects that a data flow promotes the priority of its packets, it utilizes a disorder-free flow scheduling mechanism based on dynamic prioritization to make sure that packets of the same flow arrive at the receiver without reordering. Through a series of experimental tests, we demonstrate that DPOT can decrease the deadline miss rate by up to 95% while reducing flow completion time by up to 45% in comparison to the state-of-the-art flow scheduling approaches.

Nowadays, the deluge of heterogeneous data generated by various cloud-based industrial applications often has to be delivered to the data center for analysis and storage. To speed up data processing thus facilitating application performance, the modern data center network offers rich parallel paths and super high bisection bandwidth for data communications between servers, expecting to provide good transmission performance for the heterogeneous data traffic caused by cloud-based industrial applications. Due to high path diversities, however, balancing the heterogeneous traffic load across multiple parallel paths for fully utilizing the offered super high bisection bandwidth is full of challenges (i.e., how to achieve high path utilization without incurring adverse impact). Although prior studies demonstrate that the flowlet-based solutions are promising to fill the bill, we argue that their rerouting operations are still inappropriate in timing and manner. This article presents HG, a load balancing scheme adopting hybrid switching granularity to make traffic rerouting. HG embeds the flow-fragment-based and flowcell-based path switching into the flowlet-based path switching, and employs state-weighted path measurement to choose paths for newly appeared flow fragments, flowcells, and flowlets. The results of numerous NS2 tests show that, compared with the state-of-the-art data center load balancing schemes, HG significantly reduces the average and tail-flow completion times for delay-sensitive flows, and the throughput of throughput-oriented flows is always maintained at high level.

To achieve low latency and high link utilization, ECN-based transport protocols (i.e., DCTCP) are widely deployed in data center networks (DCN). In multi-tenant environment, however, the newly introduced ECN-enabled TCP greatly impairs the performance of applications with out-dated and misconfigured TCP stacks. The reason is that the ECN-enabled switch fails to treat the mixed TCP traffic fairly, resulting in the distinguished performance gap between the ECN-enabled and ECN-disabled TCPs. This paper proposes DDT (Dual Dynamic Thresholds), an active queue management algorithm (AQM) to achieve the flow-level fairness for coexisting heterogeneous TCP traffic. DDT monitors the switch queue in real time, and dynamically tunes the distance between ECN-marking and packet-dropping thresholds to mitigate the aggressiveness difference between the ECN-enabled and ECN-disabled TCPs. The results of real implementations and large-scaled simulations show that DDT elegantly fills the aggressiveness gap of heterogeneous TCP traffic without disturbing their own control loops, while only introducing acceptable deployment overhead at switch.

With the explosive growth of the Internet of Things (IoT), an increasing amount of sensor data generated by soft real-time IoT applications has been moved to data centers for storage and data analysis. Large amounts of these data are required to be processed within a given deadline to ensure application performance. Therefore, meeting the transmission deadlines of data flows for soft real-time applications has always been crucial yet challenging to current data centers. Recent progress has demonstrated that adopting parallel data transmission over multipath data center network combining with effective load balancing can achieve a high bisection network bandwidth, thus speeding up the network transfer of data flows. Nevertheless, the deadline miss ratios (DMRs) of these flows are not lowered as expected since the existing load balancing schemes are naturally agnostic to the deadline requirement. They are either unable to reroute traffic flexibly or aimlessly reroute these deadline-restrained flows, regardless of their urgent levels and path conditions. To address these inefficiencies, we propose a deadline-aware load-balancing scheme, namely, DLB, which perceives the deadline requirements and helps the urgent flows to timely switch to those faster transmission paths to complete quickly. Specifically, DLB computes the urgent level for each flow in real time to judge if the switch needs to make proactive rerouting. When a flow is nonurgent, DLB does not proactively change its transmission path, leaving more available paths to those flows with higher urgent levels. When a flow becomes extremely urgent, it immediately switches to those light-loaded paths to finish its data transmission before its deadline as far as possible. Experimental results of NS2 simulations and real testbed implementations show that DLB reduces the DMRs by up to 50% compared to the state-of-the-art data center load-balancing schemes, while only induces trivial overhead during deployment.

Explicit Congestion Notification (ECN) is widely utilized in modern data centers to achieve low latency and high throughput for various applications. In recent years, however, even with the sustainable growth of link bandwidth in data centers, the switch buffer size does not increase remarkably. Consequently, the standard per-port ECN scheme suffers from excessive packet loss. Though the shared-buffer ECN scheme alleviates the packet loss, we observe that it leads to severe unfairness, which we term as the Port Starvation problem. When flows destined for some ports have aggressively occupied the shared buffer, the later-arrival flows destined for other ports will be ECN-marked unfairly and obtain significantly lower throughput. To address the port starvation problem, we design a buffer-aware fair ECN-marking (BFEM) scheme for shallow-buffered switch. BFEM leverages the shared buffer to reduce packet loss and meanwhile punishes aggressive flows by ECN marking. We evaluate BFEM with both 40Gbps P4 testbed implementation and large-scale NS2 simulation. The test results show that, by improving fairness between egress ports, BFEM increases total link utilization and reduces the average flow completion time by up to 40% compared with the state-of-the-art per-port and shared-buffer ECN marking schemes.

To improve the quality of experience for worldwide users, an increasing number of service providers deploy their services on geographically dispersed data centers, which are connected by wide area network (WAN). In the cross-datacenter networks, however, the intra- and inter-datacenter parts have different characteristics, including switch buffer depth, round-trip time and bandwidth. Besides, most of intra-DC flows belong to interactive services that require low delay while inter-DC flows typically need to achieve high throughput. Unfortunately, existing sender-based and receiver-driven transport protocols do not consider the network heterogeneity between inter- and intra- DC networks so that they fail to simultaneously achieve low latency for intra-DC flows and high throughput for inter-DC flows. This paper proposes a general hybrid congestion control mechanism called GTCP to address this problem. When the inter-DC flow detects congestion inside data center, it switches to the receiver-driven mode to avoid the impact on intra-DC flows. Otherwise, it switches back to the sender-based mode to proactively explore the available bandwidth. Besides, the intra-DC flow leverages the pausing mechanism to eliminate the queue build-up. Through a series of testbed experiments and large-scale NS2 simulations, we demonstrate that GTCP reduces flow completion time by up to 79.3% compared with existing protocols.

Data center networks typically adopt multi-rooted tree topologies to provide high bisection bandwidth. Various fine-grained load balancing schemes have been proposed to split flows across multiple paths. However, data center networks suffer from many uncertainties such as highly dynamic traffic. These uncertainties easily make network become asymmetric, resulting in significant packet reordering. Unfortunately, existing solutions passively deal with packet reordering based on a threshold and hardly adapt to asymmetric networks because of lacking the explicit reordering feedback. These solutions either fail to quickly respond to packet loss or cause unnecessary fast retransmission, which reduces link utilization and increases flow completion time. In this paper, we propose a fine-grained load balancing scheme RMC to eliminate the impact of packet reordering and handle uncertainties in asymmetric networks. To avoid unnecessary fast retransmission, the switch proactively identifies reordered packet according to local queue length and global path latency. Furthermore, we employ a coding technique with redundancy optimization to reduce long-tailed flow completion time under network asymmetry. Through a series of large-scale NS2 simulations and testbed experiments, we demonstrate that RMC effectively avoids unnecessary fast retransmission under different network scenarios and reduces flow completion time by up to 72% compared with state-of-the-art schemes.

In data center networks, many network-intensive applications leverage large fan-in and many-to-one communication to achieve high performance. However, the special traffic patterns, such as micro-burst and high concurrency, easily cause TCP Incast problem and seriously degrade the application performance. To address the TCP Incast problem, we first reveal theoretically and empirically that alleviating packet burstiness is much more effective in reducing the Incast probability than controlling the congestion window. Inspired by the findings and insights from our experimental observations, we further propose a general supporting scheme Adaptive Pacing (AP), which dynamically adjusts burstiness according to the flow concurrency without any change on switch. Additionally, a sender-based approach is proposed to estimate the flow concurrency. Another feature of AP is its broad applicability. We integrate AP transparently into different TCP protocols (i.e., DCTCP, L2DCT and D2TCP). Through a series of large-scale NS2 simulations and testbed experiments, we show that AP significantly reduces the Incast probability across different TCP protocols and the network goodput can be increased consistently by on average 7× under severe congestion.

To deliver high quality video across different network conditions, the video chunks are explicitly fetched by client or proactively pushed by server in Dynamic Adaptive Streaming over HTTP (DASH) system. Unfortunately, on the one hand, the client fetch mechanism suffers from bandwidth wastage due to its stop-and-wait fashion when the network delay becomes large. On the other hand, the server push mechanism performs poorly because of its inflexibility in bitrate switching under fluctuating bandwidth. To address these inefficiencies, we propose a pipeline-based chunk scheduling scheme called PCS to auto-turn the sending time of each chunk. For a given ABR algorithm, PCS dynamically pre-schedules the chunk delivery according to the real-time network conditions. Using the pipelined-based chunk delivery, PCS flexibly adjusts the bitrate of each chunk and meanwhile avoids the unnecessary waiting time in the stop-and-wait transmission. The experimental results of testbed implementations show that PCS greatly improves the average bitrate of the state-of-the-art ABR algorithms by up to 26%, and reduces the rebuffer rate by up to 31%.

To achieve better network performance, the cloud service providers are widely deploying the ECN-based transport protocols (i.e., DCTCP) in their data center networks (DCN). In multi-tenant environment, however, the newly introduced ECN-enabled TCP greatly impairs the performance of applications with out-dated and miscon figured TCP stacks. The reason is that the ECN-enabled datacenter switch fails to treat the mixed TCP traffic fairly, causing the distinguished performance gap between the ECN-enabled and ECN-disabled TCPs. This paper proposes DDT (Dual Dynamic Thresholds), an active queue management algorithm (AQM) that aims to achieve the flow-level fairness when the heterogeneous TCP traffic coexists. DDT monitors the switch queue in real time, and dynamically tunes the distance between ECN-marking and packet-dropping thresholds to mitigate the competitiveness difference between the ECN-enabled and ECN-disabled TCP. Our preliminary real implementations and testing results show that DDT elegantly fills the competitiveness gap of heterogeneous TCP traffic without disturbing their own control loops, while only introducing acceptable deployment overhead at the switch.

Modern data center networks provide multiple paths between host pairs to guarantee high aggregated network bandwidth and transmission reliability. However, data center networks suffer from various uncertainties such as highly dynamic traffic and heterogeneous devices. These uncertainties easily lead to network asymmetry and cause significant packet reordering. Unfortunately, due to lacking the explicit reordering feedback, existing sender-based and receiver-based solutions hardly adapt to asymmetric data center networks and cause long-tailed flow completion time as well as throughput loss. In this paper, we propose a per-packet transmission scheme RMC to eliminate the impact of packet reordering and handle uncertainties in asymmetric networks. To avoid unnecessary fast retransmission, the switch proactively identifies packet reordering according to local queue length and global path latency. Furthermore, we employ a coding technique to reduce long-tailed flow completion time under network asymmetry. Through a series of large-scale NS2 simulations and testbed experiments, we demonstrate that RMC reduces flow completion time by up to 72% compared with existing protocols.

In data center networks, many network-intensive applications leverage large fan-in and many-to-one communication to achieve high performance. However, the special traffic patterns, such as micro-burst and high concurrency, easily cause TCP Incast problem and seriously degrade the application performance. To address the TCP Incast problem, we first reveal theoretically and empirically that alleviating packet burstiness is much more effective in reducing the Incast probability than controlling the congestion window. Inspired by the findings and insights from our experimental observations, we further propose a general supporting scheme Adaptive Pacing (AP), which dynamically adjusts burstiness according to the flow concurrency without any change on switch. Another feature of AP is its broad applicability. We integrate AP transparently into different TCP protocols (i.e., DCTCP, L2DCT and D2TCP). Through a series of large-scale NS2 simulations, we show that AP significantly reduces the Incast probability across different TCP protocols and the network goodput can be increased consistently by on average 7x under severe congestion.