# US 9,436,917 B2 · Accelerating learning by sharing information between multiple learning machines > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,436,917 B2 - Document ID: US9436917B2 - Status: Granted - Kind: B2 - Application number: 13/937,631 - Filed: 2013-07-09 - Published: 2014-08-07 - Granted: 2016-09-06 - Earliest claimed: 2013-02-05 - Assignees: Cisco Technology, Inc. - Applicants: Cisco Technology, Inc. - Inventors: Grégory Mermoud; Jean-Philippe Vasseur; Sukrit Dasgupta - Web page: https://patentgrep.com/patent/US9436917B2 ## CPC - G06N 20/00: Machine learning - H04L 61/00: Network arrangements, protocols or services for addressing or naming - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract In one embodiment, variables maintained by each of a plurality of Learning Machines (LMs) are determined. The LMs are hosted on a plurality of Field Area Routers (FARs) in a network, and the variables are sharable between the FARs. A plurality of correlation values defining a correlation between the variables is calculated. Then, a cluster of FARs is computed based on the plurality of correlation values, such that the clustered FARs are associated with correlated variables, and the cluster allows the clustered FARs to share their respective variables. ## Claims ### Claim 1 A method, comprising: determining, by an agent device, variables maintained by each of a plurality of Learning Machines (LMs), which are hosted on a plurality of Field Area Routers (FARs) in a network, wherein the variables are sharable between the FARs, wherein the plurality of FARs each connect a local area network with a wide area network; calculating, by the agent device, a plurality of correlation values defining a correlation between the variables; computing, by the agent device, a cluster of FARs based on the plurality of correlation values, wherein the clustered FARs are associated with correlated variables, and the cluster allows the clustered FARs to share their respective variables; and sending, from the agent device, a message to each FAR in the computed cluster FAR, the message indicating to each FAR receiving the message that that FAR is in the computer clustered FAR computed by the agent device. ### Claim 2 (depends on 1) The method according to claim 1, further comprising: determining whether a correlation value of the plurality of correlation values exceeds a predetermined threshold, wherein the cluster is computed only when the correlation value exceeds the predetermined threshold. ### Claim 3 (depends on 1) The method according to claim 1, wherein the determining of the variables comprises: periodically transmitting messages to each FAR to probe the FARs for updated variables. ### Claim 4 (depends on 1) The method according to claim 1, further comprising: determining a sharing rate defining a rate at which the variables are shared between the FARs in the cluster of FARs. ### Claim 5 (depends on 4) The method according to claim 4, further comprising: determining available network resources in the network, wherein the sharing rate is determined based on the available network resources. ### Claim 6 (depends on 4) The method according to claim 4, further comprising: receiving an indication of a user-defined sharing rate, wherein the sharing rate is determined based on the user-defined sharing rate. ### Claim 7 (depends on 1) The method according to claim 1, further comprising: dynamically adjusting the cluster based on a network metric. ### Claim 8 (depends on 7) The method according to claim 7, further comprising: receiving feedback from a particular LM relating to variables maintained by the particular LM, wherein the network metric is based on the received feedback. ### Claim 9 (depends on 7) The method according to claim 7, further comprising: determining available network resources in the network, wherein the network metric is based on the available network resources. ### Claim 10 An apparatus, comprising: one or more network interfaces that communicate with a network; a processor coupled to the one or more network interfaces and configured to execute a process; and a memory configured to store program instructions which contain the process executable by the processor, the process comprising: determining variables maintained by each of a plurality of Learning Machines (LMs), which are hosted on a plurality of Field Area Routers (FARs) in the network, wherein the variables are sharable between the FARs, wherein the plurality of FARs each connect a local area network with a wide area network; calculating a plurality of correlation values defining a correlation between the variables; computing a cluster of FARs based on the plurality of correlation values, wherein the clustered FARs are associated with correlated variables, and the cluster allows the clustered FARs to share their respective variables; and sending a message to each FAR in the computed cluster FAR, the message indicating to each FAR receiving the message that that FAR is in the computer clustered FAR computed by the apparatus. ### Claim 11 (depends on 10) The apparatus according to claim 10, wherein the process further comprises: determining whether a correlation value of the plurality of correlation values exceeds a predetermined threshold, wherein the cluster is computed only when the correlation value exceeds the predetermined threshold. ### Claim 12 (depends on 10) The apparatus according to claim 10, wherein the determining of the variables comprises: periodically transmitting messages to each FAR to probe the FARs for updated variables. ### Claim 13 (depends on 10) The apparatus according to claim 10, wherein the process further comprises: determining a sharing rate defining a rate at which the variables are shared between the FARs in the cluster of FARs. ### Claim 14 (depends on 13) The apparatus according to claim 13, wherein the process further comprises: determining available network resources in the network, wherein the sharing rate is determined based on the available network resources. ### Claim 15 (depends on 13) The apparatus according to claim 13, wherein the process further comprises: receiving an indication of a user-defined sharing rate, wherein the sharing rate is determined based on the user-defined sharing rate. ### Claim 16 (depends on 10) The apparatus according to claim 10, wherein the process further comprises: dynamically adjusting the cluster based on a network metric. ### Claim 17 (depends on 16) The apparatus according to claim 16, wherein the process further comprises: receiving feedback from an LM relating to variables maintained by the LM, wherein the network metric is based on the received feedback. ### Claim 18 (depends on 16) The apparatus according to claim 16, wherein the process further comprises: determining available network resources in the network, wherein the network metric is based on the available network resources. ### Claim 19 A tangible non-transitory computer readable medium storing program instructions that cause a computer to execute a process, the process comprising: determining variables maintained by each of a plurality of Learning Machines (LMs), which are hosted on a plurality of Field Area Routers (FARs) in a network, wherein the variables are sharable between the FARs, wherein the plurality of FARs each connect a local area network with a wide area network; calculating a plurality of correlation values defining a correlation between the variables; computing a cluster of FARs based on the plurality of correlation values, wherein the clustered FARs are associated with correlated variables, and the cluster allows the clustered FARs to share their respective variables; and sending a message to each FAR in the computed cluster FAR, the message indicating to each FAR receiving the message that that FAR is in the computer clustered FAR computed by the agent device. ### Claim 20 (depends on 19) The computer readable medium according to claim 19, further comprising: determining whether a correlation value of the plurality of correlation values exceeds a predetermined threshold, wherein the cluster is computed only when the correlation value exceeds the predetermined threshold.